Grafana MCP Server

Q: What can Grafana MCP Server do?

mcp protocol server with multi-transport bridging, datasource-agnostic query execution with multi-provider support, opentelemetry tracing and prometheus metrics observability, tool schema discovery and dynamic tool registration, grafana variable resolution and dashboard context propagation, grafana folder and permission-aware resource navigation, pyroscope profiling data querying and trace analysis, grafana user and organization management querying, context window optimization and token usage tracking, dashboard discovery and metadata retrieval with search filtering, panel data extraction and visualization query execution, prometheus-native metric querying with promql support, loki log querying with logql and log stream filtering, alert rule management and alert state querying, annotation creation and retrieval with time-series correlation, oncall incident management and escalation policy querying, multi-organization and multi-tenant session management

MCP ServerFree

Query Grafana dashboards, datasources, and alerts via MCP.

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17 capabilities

Capabilities17 decomposed

mcp protocol server with multi-transport bridging

Medium confidence

Implements the Model Context Protocol as a Go-based server using the mark3labs/mcp-go framework, supporting three transport modes (stdio for direct process integration, SSE for server-sent events, and streamable-http for stateless deployments). The server exposes Grafana capabilities as standardized MCP tools that AI assistants can discover and invoke through a unified interface, abstracting away Grafana API complexity behind tool schemas.

Solves for

I want to connect Claude or another AI assistant to my Grafana instance without building custom integrationsI need to expose Grafana as a tool that an LLM agent can call programmaticallyI want to support multiple transport modes (stdio, SSE, HTTP) depending on my deployment architecture

Best for

DevOps teams integrating Grafana with AI-powered observability workflows

Developers building LLM agents that need standardized access to monitoring data

Organizations deploying MCP servers in containerized or serverless environments

Requires

Go 1.19+ (for server compilation)

Grafana instance with API access (any version with REST API support)

MCP-compatible client (Claude Desktop, or custom MCP client implementation)

Limitations

Transport mode selection is fixed at startup — cannot switch between stdio/SSE/HTTP at runtime

Multi-tenant deployments require SessionManager configuration per HTTP request, adding per-request overhead

MCP protocol version compatibility depends on client implementation — older clients may not support all tool features

What makes it unique

Official Grafana implementation using mark3labs/mcp-go framework with built-in support for three transport modes (stdio, SSE, streamable-http) and SessionManager for multi-tenant scenarios, rather than generic MCP wrappers that require custom transport configuration

vs alternatives

Provides native Grafana API integration with official support and maintenance, whereas third-party MCP servers require custom Grafana API bindings and lack official updates

datasource-agnostic query execution with multi-provider support

Medium confidence

Exposes a unified query interface that routes requests to Grafana's datasource abstraction layer, supporting Prometheus, Loki, Pyroscope, Elasticsearch, CloudWatch, and other configured datasources. The server translates MCP tool parameters into datasource-specific query formats, handles authentication delegation to Grafana, and returns results in a normalized structure. This abstraction allows AI assistants to query any datasource without knowing its native query language.

Solves for

I want to query metrics from Prometheus without writing PromQL directlyI need to search logs in Loki using natural language that gets translated to LogQLI want to execute queries across multiple datasources without managing separate API keys or authentication

Best for

Teams with heterogeneous datasource stacks (Prometheus + Loki + Elasticsearch)

Non-expert users who want to query observability data without learning PromQL/LogQL

AI agents that need to dynamically select datasources based on query intent

Requires

Grafana instance with at least one configured datasource

API key or service account token with datasource query permissions

Datasource-specific credentials already configured in Grafana (MCP server does not manage datasource auth directly)

Limitations

Query translation is datasource-specific — complex queries may lose nuance when converted from natural language to PromQL/LogQL

Datasource availability depends on Grafana configuration — if a datasource is not configured in Grafana, it cannot be queried

Authentication is delegated to Grafana, so datasource-level RBAC is inherited from Grafana's permission model

What makes it unique

Implements datasource abstraction through Grafana's native datasource plugin architecture, allowing the MCP server to support any datasource Grafana supports (20+ types) without custom code, rather than hardcoding support for specific datasources

vs alternatives

Supports any datasource configured in Grafana automatically, whereas point-to-point integrations require separate tool implementations for each datasource type

opentelemetry tracing and prometheus metrics observability

Medium confidence

Integrates OpenTelemetry tracing and Prometheus metrics collection into the MCP server itself, allowing operators to observe MCP server behavior, tool execution latency, and error rates. The server exports traces to configured OpenTelemetry backends and exposes Prometheus metrics on a metrics endpoint. This enables operators to monitor the MCP server's health and performance without external instrumentation.

Solves for

I want to monitor the latency of MCP tool executionsI need to track error rates and failure modes in the MCP serverI want to understand which tools are being used most frequently

Best for

Operators managing production MCP server deployments

Teams that need to monitor MCP server performance and reliability

Organizations with existing OpenTelemetry and Prometheus infrastructure

Requires

OpenTelemetry collector or backend (optional, for trace export)

Prometheus scrape configuration (optional, for metrics collection)

Environment variables or configuration file for OpenTelemetry setup

Limitations

Tracing and metrics are opt-in — require explicit configuration to enable

OpenTelemetry export adds overhead to request processing — may impact latency for high-frequency queries

Metrics are exposed on a separate HTTP endpoint — requires additional network configuration

What makes it unique

Integrates OpenTelemetry tracing and Prometheus metrics natively into the MCP server, providing built-in observability without external instrumentation, rather than requiring separate monitoring tools or custom logging

vs alternatives

Provides native observability integration with OpenTelemetry and Prometheus, whereas generic MCP servers require custom instrumentation or external monitoring

tool schema discovery and dynamic tool registration

Medium confidence

Implements a tool management framework that dynamically discovers and registers MCP tools based on Grafana configuration and datasource availability. The server exposes tool schemas through the MCP protocol, allowing clients to discover available tools, their parameters, and expected outputs. Tools are registered at startup based on configured datasources and Grafana features, and the schema includes validation rules, parameter descriptions, and example usage.

Solves for

I want to discover what tools are available in the MCP server without reading documentationI need to understand the parameters and expected outputs of a specific toolI want to dynamically adapt my AI agent based on available tools

Best for

AI agents that need to discover available tools at runtime

Developers building MCP clients that need to adapt to different server configurations

Teams that want to expose different tools based on Grafana configuration

Requires

MCP client that supports tool discovery (most modern MCP clients)

Grafana instance with configured datasources and features

Limitations

Tool schemas are static after server startup — changes to Grafana configuration require server restart

Tool discovery is limited to tools exposed by the MCP server — no discovery of Grafana UI features

Schema validation is basic — complex parameter constraints may not be expressible in tool schemas

What makes it unique

Implements dynamic tool registration based on Grafana datasource configuration, allowing tools to be discovered and registered at startup without hardcoding tool lists, rather than requiring manual tool schema definition

vs alternatives

Provides automatic tool discovery based on Grafana configuration, whereas static MCP servers require manual tool schema definition and updates

grafana variable resolution and dashboard context propagation

Medium confidence

Provides tools to resolve Grafana dashboard variables (template variables) and propagate them through query execution. The server retrieves variable definitions from dashboards, resolves variable values based on current selections or defaults, and injects resolved values into queries executed against dashboard panels. This enables AI assistants to execute queries with the correct variable context without manually managing variable resolution.

Solves for

I want to execute a dashboard panel query with the current variable selectionsI need to resolve a dashboard variable to its current valueI want to execute a query with different variable values than the dashboard default

Best for

Incident investigation workflows that need to execute dashboard queries with current context

AI agents analyzing dashboards with complex variable configurations

Automation that needs to programmatically resolve and use dashboard variables

Requires

Dashboard ID with variable definitions

Grafana API key with dashboard read permissions

Limitations

Variable resolution is limited to variables defined in the dashboard — no cross-dashboard variable references

Dynamic variable values (from queries) require executing the variable query — adds latency

Variable cascading (where one variable depends on another) requires sequential resolution

What makes it unique

Implements dashboard variable resolution and propagation through query execution, allowing AI assistants to execute queries with correct variable context without manual variable management, rather than requiring users to manually resolve variables

vs alternatives

Provides automatic variable resolution based on dashboard definitions, whereas generic query tools require manual variable substitution

grafana folder and permission-aware resource navigation

Medium confidence

Provides tools to navigate Grafana's folder hierarchy and respect permission boundaries when listing resources (dashboards, datasources, alert rules). The server queries Grafana's folder API and applies RBAC filters based on the authenticated user's permissions, ensuring that only accessible resources are returned. This enables AI assistants to navigate Grafana's resource hierarchy while respecting organizational access controls.

Solves for

I want to list all folders and their contents that I have access toI need to find a dashboard within a specific folder hierarchyI want to understand the permission structure of Grafana resources

Best for

Large Grafana deployments with complex folder hierarchies and RBAC

Teams that need to enforce permission boundaries in AI-driven workflows

Automation that needs to respect Grafana's access control model

Requires

Grafana instance with folder structure configured

API key with folder and resource read permissions

Limitations

Folder navigation is limited to folders the user has read access to — no discovery of restricted folders

Permission inheritance is complex — folder permissions may be overridden at resource level

Folder hierarchy depth may be large — listing all folders could be slow for deeply nested structures

What makes it unique

Implements permission-aware resource navigation that respects Grafana's RBAC model, ensuring AI assistants only access resources the user has permission to view, rather than exposing all resources regardless of permissions

vs alternatives

Provides permission-aware resource discovery that enforces Grafana's access control, whereas generic API clients require manual permission filtering

pyroscope profiling data querying and trace analysis

Medium confidence

Provides specialized tools for querying Pyroscope profiling datasources, including profile data retrieval, flame graph generation, and performance hotspot identification. The server translates MCP tool parameters into Pyroscope API calls and returns profiling data in a format suitable for analysis. This enables AI assistants to analyze application performance profiles and identify optimization opportunities.

Solves for

I want to retrieve CPU profiling data for a specific service and time rangeI need to identify performance hotspots in application codeI want to compare profiling data across different time periods or versions

Best for

Performance engineering teams that need programmatic access to profiling data

AI agents analyzing application performance for optimization recommendations

Incident response workflows that need to correlate performance issues with metrics

Requires

Grafana instance with Pyroscope datasource configured

Pyroscope instance with profiling data collection enabled

API key with Pyroscope query permissions

Limitations

Profiling data is only available if Pyroscope is configured as a datasource in Grafana

Profile data can be large — result size limits may truncate detailed profiling information

Flame graph generation requires client-side rendering — MCP server returns raw profiling data

What makes it unique

Exposes Pyroscope profiling API through MCP tools, allowing AI assistants to query and analyze profiling data without direct Pyroscope API access, rather than requiring separate profiling tool integrations

vs alternatives

Provides native Pyroscope integration with profiling data querying, whereas generic profiling tools require separate integrations and lack Grafana context

grafana user and organization management querying

Medium confidence

Provides tools to query Grafana user and organization information, including user lists, organization membership, and role assignments. The server queries Grafana's admin API to expose user and organization data. This enables AI assistants to understand Grafana's organizational structure and user permissions without accessing the Grafana UI.

Solves for

I want to see all users in a Grafana organizationI need to understand a user's role and permissionsI want to list all organizations in a Grafana instance

Best for

Grafana administrators that need programmatic access to user and organization data

Automation workflows that need to manage Grafana users and permissions

AI agents that need to understand organizational structure for access control

Requires

Grafana instance with admin API enabled

API key with admin permissions (for user and organization queries)

Limitations

User and organization management (create/update/delete) is not supported — read-only access only

User data is limited to information exposed by Grafana API — no custom user attributes

Organization membership queries are limited to organizations the user has access to

What makes it unique

Exposes Grafana admin API for user and organization querying through MCP tools, allowing programmatic access to organizational structure without direct admin API access, rather than requiring separate admin tools

vs alternatives

Provides native Grafana admin integration with user and organization querying, whereas third-party admin tools require separate integrations and lack Grafana context

context window optimization and token usage tracking

Medium confidence

Implements context window management strategies to optimize LLM token usage when working with large dashboard definitions, query results, and profiling data. The server provides tools to estimate token usage, truncate results intelligently, and summarize large datasets. Token usage is tracked and exposed through observability metrics, allowing operators to understand and optimize context consumption.

Solves for

I want to understand how many tokens a dashboard definition will consumeI need to retrieve large query results without exceeding my LLM's context windowI want to summarize large profiling datasets for analysis

Best for

Teams using expensive LLM APIs that charge per token

AI agents with limited context windows that need to work with large datasets

Operators that need to track and optimize token usage

Requires

OpenTelemetry setup (optional, for token tracking)

LLM tokenizer configuration (for accurate token estimation)

Limitations

Token estimation is approximate — actual token usage depends on LLM tokenizer

Result truncation may lose important data — intelligent summarization requires domain knowledge

Context window optimization is not automatic — requires explicit tool invocation

What makes it unique

Implements context window management and token usage tracking natively in the MCP server, allowing AI assistants to optimize token consumption without external tools, rather than requiring manual context management

vs alternatives

Provides built-in context window optimization and token tracking, whereas generic MCP servers require manual context management and external token counting tools

dashboard discovery and metadata retrieval with search filtering

Medium confidence

Provides tools to list all dashboards accessible to the authenticated user, search dashboards by name/tag/folder, and retrieve full dashboard definitions including panel configurations, datasource references, and variable definitions. The server queries Grafana's dashboard API and returns structured metadata that AI assistants can use to understand available observability resources and select relevant dashboards for analysis.

Solves for

I want to find dashboards related to 'database performance' without manually browsing Grafana UII need to retrieve the full definition of a dashboard to understand which metrics it usesI want to list all dashboards in a specific folder or with a specific tag

Best for

AI agents that need to discover relevant dashboards before executing queries

Teams with large numbers of dashboards (100+) that need programmatic discovery

Automation workflows that need to reference dashboard definitions

Requires

Grafana API key or service account token with dashboard read permissions

Grafana instance with at least one dashboard

Limitations

Search is limited to dashboard name, tags, and folder — does not search panel content or queries

Dashboard definitions can be large (100KB+) for complex dashboards with many panels, impacting token usage in LLM context

Folder hierarchy is flattened in search results — no tree-based folder navigation

What makes it unique

Exposes Grafana's native dashboard search and retrieval APIs through MCP tools, allowing AI assistants to discover and analyze dashboard definitions programmatically, rather than requiring manual UI navigation or custom scraping

vs alternatives

Provides structured access to full dashboard definitions including panel queries and datasource references, whereas generic Grafana API clients require manual parsing of dashboard JSON

panel data extraction and visualization query execution

Medium confidence

Allows retrieval of data from specific dashboard panels by executing the panel's configured queries against its datasources. The server extracts query definitions from a dashboard panel, executes them with the specified time range, and returns the raw data that would be visualized in the panel. This enables AI assistants to analyze the exact data powering a dashboard visualization without needing to understand the underlying query language.

Solves for

I want to get the raw data from a specific panel in a dashboardI need to execute a panel's query with a different time range than what's currently displayedI want to understand what data is being visualized in a dashboard panel

Best for

Incident investigation workflows that need to extract data from specific dashboard panels

AI agents analyzing dashboard data to generate insights or alerts

Automation that needs to programmatically access panel data for further processing

Requires

Dashboard ID and panel ID

Grafana API key with dashboard and datasource query permissions

Time range specification (relative or absolute)

Limitations

Panel data extraction is limited to panels with queryable datasources — visualization-only panels (text, static content) return no data

Query execution inherits datasource timeout and result size limits — very large result sets may be truncated

Panel variables must be resolved before query execution — dynamic variable resolution requires additional context

What makes it unique

Extracts and executes panel-specific queries from dashboard definitions, allowing AI assistants to retrieve visualization data without manually reconstructing queries, rather than requiring users to know the underlying PromQL/LogQL

vs alternatives

Provides direct access to panel data through the MCP interface, whereas generic Grafana API clients require manual dashboard parsing and query reconstruction

prometheus-native metric querying with promql support

Medium confidence

Provides specialized tools for querying Prometheus datasources using PromQL, including metric name completion, label value lookup, and instant/range query execution. The server translates MCP tool parameters into Prometheus API calls (instant queries, range queries, label queries) and returns results in Prometheus native format. This enables AI assistants to leverage Prometheus's full query capabilities while maintaining the MCP abstraction.

Solves for

I want to execute a PromQL query against Prometheus without managing the HTTP API directlyI need to discover available metric names and labels in PrometheusI want to get metric values at a specific point in time or over a time range

Best for

Teams using Prometheus as their primary metrics backend

AI agents that need to execute complex PromQL queries for analysis

Incident response workflows that need rapid metric exploration

Requires

Grafana instance with Prometheus datasource configured

Prometheus instance accessible from MCP server

API key with Prometheus query permissions

Limitations

PromQL query validation is delegated to Prometheus — invalid queries fail at execution time, not at tool invocation

Metric cardinality limits are inherited from Prometheus configuration — high-cardinality queries may timeout

Label value lookups are limited to labels present in the Prometheus instance — no cross-datasource label discovery

What makes it unique

Exposes Prometheus API endpoints through MCP tools with PromQL support, allowing AI assistants to execute complex metric queries while maintaining the MCP abstraction, rather than requiring direct Prometheus API access

vs alternatives

Provides native PromQL support with metric completion and label discovery, whereas generic Grafana datasource tools require users to construct PromQL manually

loki log querying with logql and log stream filtering

Medium confidence

Provides specialized tools for querying Loki datasources using LogQL, including log stream label discovery, log entry retrieval, and metric extraction from logs. The server translates MCP tool parameters into Loki API calls (query_range, query, labels, label_values) and returns results in Loki native format. This enables AI assistants to search and analyze logs programmatically without requiring knowledge of LogQL syntax.

Solves for

I want to search logs for a specific error message or patternI need to retrieve logs from a specific service or pod over a time rangeI want to discover available log labels and streams in Loki

Best for

Teams using Loki for log aggregation and analysis

Incident investigation workflows that need to correlate logs with metrics

AI agents analyzing logs to identify root causes or patterns

Requires

Grafana instance with Loki datasource configured

Loki instance accessible from MCP server

API key with Loki query permissions

Limitations

LogQL query validation is delegated to Loki — invalid queries fail at execution time

Log result size is limited by Loki max_cache_freshness_per_query — very large log volumes may be truncated

Label discovery is limited to labels present in Loki — no cross-datasource label discovery

What makes it unique

Exposes Loki API endpoints through MCP tools with LogQL support and log stream filtering, allowing AI assistants to search and analyze logs without requiring LogQL knowledge, rather than requiring direct Loki API access

vs alternatives

Provides native LogQL support with label discovery and log stream filtering, whereas generic log query tools require users to construct LogQL manually or use simple text search

alert rule management and alert state querying

Medium confidence

Provides tools to list alert rules configured in Grafana, retrieve alert rule definitions, query current alert states, and retrieve alert history. The server queries Grafana's alerting API (unified alerting) to expose alert rules, their conditions, and current firing status. This enables AI assistants to understand the alerting landscape and investigate active alerts without accessing the Grafana UI.

Solves for

I want to see all active alerts and their current statusI need to understand the conditions and thresholds of a specific alert ruleI want to retrieve the history of a specific alert over a time range

Best for

Incident response teams that need programmatic access to alert state

AI agents that need to correlate alerts with metrics and logs

Automation workflows that need to query alert status for decision-making

Requires

Grafana instance with unified alerting enabled

API key with alert rule read permissions

Limitations

Alert rule management (create/update/delete) is not supported — read-only access only

Alert history is limited by Grafana's retention policy — older alerts may not be available

Alert state is point-in-time — no streaming or real-time alert notifications through MCP

What makes it unique

Exposes Grafana's unified alerting API through MCP tools, providing programmatic access to alert rules and state without requiring manual UI navigation, rather than requiring custom alerting integrations

vs alternatives

Provides native Grafana alerting integration with support for unified alerting rules, whereas third-party alert tools require separate integrations for each alerting system

annotation creation and retrieval with time-series correlation

Medium confidence

Provides tools to create annotations (events/markers) in Grafana dashboards and retrieve existing annotations within a time range. The server translates MCP tool parameters into Grafana annotation API calls, allowing AI assistants to mark significant events (deployments, incidents, etc.) on dashboards and retrieve annotations for correlation with metrics and logs. Annotations are stored in Grafana's annotation storage and can be visualized across dashboards.

Solves for

I want to create an annotation marking a deployment event on a dashboardI need to retrieve all annotations within a time range to correlate with metric anomaliesI want to add incident markers to dashboards for post-incident analysis

Best for

Incident response workflows that need to mark events for correlation analysis

Automation that needs to record deployment or configuration change events

AI agents analyzing time-series data and needing to correlate with external events

Requires

Grafana instance with annotation storage enabled

API key with annotation write permissions (for creation) or read permissions (for retrieval)

Limitations

Annotation creation requires write permissions — read-only API keys cannot create annotations

Annotations are dashboard-specific or global — no cross-dashboard annotation queries

Annotation storage is limited by Grafana's retention policy — old annotations may be deleted

What makes it unique

Exposes Grafana's annotation API through MCP tools, allowing AI assistants to create and retrieve annotations for event correlation without manual UI interaction, rather than requiring custom event logging systems

vs alternatives

Provides native Grafana annotation integration with time-series correlation, whereas external event tracking systems require separate integrations and lack dashboard visualization

oncall incident management and escalation policy querying

Medium confidence

Provides tools to query Grafana OnCall incident management data, including on-call schedules, escalation policies, current incidents, and incident history. The server queries Grafana OnCall's API to expose incident state, on-call assignments, and escalation information. This enables AI assistants to understand incident context and on-call status without accessing the OnCall UI.

Solves for

I want to see who is currently on-call for a specific serviceI need to retrieve the current incident status and escalation chainI want to understand the on-call schedule and escalation policies

Best for

Incident response teams that need programmatic access to on-call information

AI agents that need to route incidents to the correct on-call engineer

Automation workflows that need to query on-call status for decision-making

Requires

Grafana Cloud or self-hosted OnCall instance

OnCall API key with read permissions

OnCall integration configured in Grafana

Limitations

OnCall integration requires Grafana Cloud or self-hosted OnCall instance — not available in open-source Grafana

Incident creation/update is not supported — read-only access only

On-call schedule queries are point-in-time — no future schedule predictions

What makes it unique

Integrates Grafana OnCall API through MCP tools, providing programmatic access to incident and on-call data without requiring separate OnCall API clients, rather than requiring custom incident management integrations

vs alternatives

Provides native OnCall integration with incident and escalation policy querying, whereas third-party incident tools require separate integrations and lack Grafana context

multi-organization and multi-tenant session management

Medium confidence

Implements SessionManager pattern for HTTP-based transports (SSE, streamable-http) that allows per-request configuration of Grafana instance URL, API key, and organization context. The server maintains session state per HTTP request, enabling multi-tenant deployments where different clients connect to different Grafana instances or organizations. Session configuration is passed through MCP request headers or initialization parameters, allowing dynamic tenant switching without server restart.

Solves for

I want to deploy a single MCP server that serves multiple Grafana instances or organizationsI need to route requests from different clients to different Grafana instances dynamicallyI want to support per-request authentication without hardcoding credentials

Best for

Multi-tenant SaaS platforms that need to serve multiple Grafana instances

Enterprise deployments with multiple Grafana organizations

Managed service providers offering Grafana access to multiple customers

Requires

HTTP-based transport (SSE or streamable-http)

Per-request session configuration (Grafana URL, API key, organization ID)

Grafana instances with API access for each tenant

Limitations

Session management is HTTP-only — stdio transport does not support multi-tenant configuration

Session state is per-request — no cross-request session persistence (stateless design)

Organization context is inherited from Grafana API key permissions — no additional RBAC enforcement at MCP layer

What makes it unique

Implements SessionManager pattern for per-request multi-tenant configuration in HTTP transports, allowing a single MCP server to serve multiple Grafana instances without hardcoding credentials, rather than requiring separate server instances per tenant

vs alternatives

Provides stateless multi-tenant support through session management, whereas single-tenant MCP servers require separate deployments for each Grafana instance

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Best For

✓DevOps teams integrating Grafana with AI-powered observability workflows
✓Developers building LLM agents that need standardized access to monitoring data
✓Organizations deploying MCP servers in containerized or serverless environments
✓Teams with heterogeneous datasource stacks (Prometheus + Loki + Elasticsearch)
✓Non-expert users who want to query observability data without learning PromQL/LogQL
✓AI agents that need to dynamically select datasources based on query intent
✓Operators managing production MCP server deployments
✓Teams that need to monitor MCP server performance and reliability

Known Limitations

⚠Transport mode selection is fixed at startup — cannot switch between stdio/SSE/HTTP at runtime
⚠Multi-tenant deployments require SessionManager configuration per HTTP request, adding per-request overhead
⚠MCP protocol version compatibility depends on client implementation — older clients may not support all tool features
⚠Query translation is datasource-specific — complex queries may lose nuance when converted from natural language to PromQL/LogQL
⚠Datasource availability depends on Grafana configuration — if a datasource is not configured in Grafana, it cannot be queried
⚠Authentication is delegated to Grafana, so datasource-level RBAC is inherited from Grafana's permission model

Requirements

Go 1.19+ (for server compilation)Grafana instance with API access (any version with REST API support)MCP-compatible client (Claude Desktop, or custom MCP client implementation)Network connectivity from MCP server to Grafana instanceGrafana instance with at least one configured datasourceAPI key or service account token with datasource query permissionsDatasource-specific credentials already configured in Grafana (MCP server does not manage datasource auth directly)OpenTelemetry collector or backend (optional, for trace export)

Input / Output

Accepts: MCP tool invocation requests with JSON-serialized parameters, Grafana API credentials (API key or OAuth token), Query parameters and filter specifications, Query parameters (datasource ID, query string, time range), Optional: datasource-specific query format (PromQL, LogQL, etc.), Time range specifications (relative or absolute), OpenTelemetry configuration (collector endpoint, sampling rate), Prometheus metrics configuration (port, path), MCP tool list request, Dashboard ID, Variable name (optional, for resolving specific variables), Variable value overrides (optional, for custom variable values), Folder ID (optional, for listing folder contents), Resource type filter (optional, for filtering by resource type), Service or application name, Profile type (cpu, memory, goroutine, etc.), Time range (relative or absolute timestamps), Organization ID (optional, for querying organization-specific users), User ID or email (optional, for querying specific user), Data to estimate (dashboard definition, query results, etc.), Target context window size (optional, for result truncation), Search query string (optional, for filtering by name), Tag filter (optional, for filtering by tags), Folder ID (optional, for filtering by folder), Dashboard ID (for retrieving specific dashboard definition), Dashboard ID (string), Panel ID (string or number), Time range (relative like '1h' or absolute timestamps), Optional: variable overrides for dashboard variables, PromQL query string, Optional: step parameter for range queries, LogQL query string or log stream selector, Optional: limit parameter for result size, Alert rule ID or name (for retrieving specific rule), Time range (for alert history queries), Optional: filter by alert state (firing, resolved, etc.), Annotation text (event description), Timestamp (when the event occurred), Optional: dashboard ID (for dashboard-specific annotations), Optional: tags (for filtering annotations), Service or team ID (for on-call schedule queries), Incident ID (for incident details), Time range (for incident history), Session configuration (Grafana URL, API key, organization ID), MCP tool invocation parameters

Produces: MCP tool result responses (JSON-structured data), Grafana API responses (dashboards, metrics, alerts, annotations), Error responses with diagnostic information, Time series data (metrics with timestamps and values), Log entries (structured or unstructured text with metadata), Trace data (from Pyroscope or similar), Structured query results in JSON format, OpenTelemetry traces (exported to configured backend), Prometheus metrics (exposed on metrics endpoint), Structured logs with trace context, Tool schema list (tool names, parameters, descriptions, examples), Variable definitions (name, type, default value), Resolved variable values, Variable options (for multi-select variables), Folder list (with hierarchy and permissions), Resource list (dashboards, datasources, alert rules within folder), Permission information (user/team permissions for resources), Profiling data (stack traces with sample counts), Flame graph data (hierarchical call stacks), Performance metrics (CPU usage, memory allocation, etc.), User list (with email, role, organization membership), User details (permissions, teams, last login), Organization list (with name and user count), Organization details (members and roles), Token count estimate, Truncated results (if result size exceeds context window), Summarized data (high-level overview of large datasets), Dashboard list with metadata (ID, title, tags, folder, last modified), Full dashboard definition (JSON) including panels, datasources, variables, annotations, Table data (rows and columns), Log entries (from log panels), Raw query results in JSON format, Instant query results (single value per metric), Range query results (time series with multiple samples), Metric names (from metric completion), Label values (from label lookup), Log entries (with timestamps and labels), Log stream labels (available label names), Label values (for a specific label), Metric results (from metric extraction queries), Alert rule definitions (conditions, thresholds, notification channels), Current alert instances (firing alerts with values), Alert history (state changes over time), Annotation ID (for created annotations), Annotation list (with timestamps, text, and tags), Annotation details (full annotation object), On-call schedule (current and upcoming assignments), Escalation policies (escalation chain and timing), Incident details (status, severity, assigned user), Incident history (state changes and timeline), MCP tool results scoped to the session's Grafana instance

UnfragileRank

Adoption70%(25% weight)

Quality90%(25% weight)

Ecosystem52%(15% weight)

Match Graph25%(30% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

17 capabilities

Visit Grafana MCP Server→

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Official Grafana MCP server for observability platform. Provides tools to query datasources, list and search dashboards, retrieve panel data, and interact with Grafana alerting and annotations.

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Capabilities17 decomposed

mcp protocol server with multi-transport bridging

Medium confidence

Solves for

Best for

DevOps teams integrating Grafana with AI-powered observability workflows

Developers building LLM agents that need standardized access to monitoring data

Organizations deploying MCP servers in containerized or serverless environments

Requires

Go 1.19+ (for server compilation)

Grafana instance with API access (any version with REST API support)

MCP-compatible client (Claude Desktop, or custom MCP client implementation)

Limitations

Transport mode selection is fixed at startup — cannot switch between stdio/SSE/HTTP at runtime

Multi-tenant deployments require SessionManager configuration per HTTP request, adding per-request overhead

MCP protocol version compatibility depends on client implementation — older clients may not support all tool features

What makes it unique

vs alternatives

Provides native Grafana API integration with official support and maintenance, whereas third-party MCP servers require custom Grafana API bindings and lack official updates

datasource-agnostic query execution with multi-provider support

Medium confidence

Solves for

Best for

Teams with heterogeneous datasource stacks (Prometheus + Loki + Elasticsearch)

Non-expert users who want to query observability data without learning PromQL/LogQL

AI agents that need to dynamically select datasources based on query intent

Requires

Grafana instance with at least one configured datasource

API key or service account token with datasource query permissions

Datasource-specific credentials already configured in Grafana (MCP server does not manage datasource auth directly)

Limitations

Query translation is datasource-specific — complex queries may lose nuance when converted from natural language to PromQL/LogQL

Datasource availability depends on Grafana configuration — if a datasource is not configured in Grafana, it cannot be queried

Authentication is delegated to Grafana, so datasource-level RBAC is inherited from Grafana's permission model

What makes it unique

vs alternatives

Supports any datasource configured in Grafana automatically, whereas point-to-point integrations require separate tool implementations for each datasource type

opentelemetry tracing and prometheus metrics observability

Medium confidence

Solves for

I want to monitor the latency of MCP tool executionsI need to track error rates and failure modes in the MCP serverI want to understand which tools are being used most frequently

Best for

Operators managing production MCP server deployments

Teams that need to monitor MCP server performance and reliability

Organizations with existing OpenTelemetry and Prometheus infrastructure

Requires

OpenTelemetry collector or backend (optional, for trace export)

Prometheus scrape configuration (optional, for metrics collection)

Environment variables or configuration file for OpenTelemetry setup

Limitations

Tracing and metrics are opt-in — require explicit configuration to enable

OpenTelemetry export adds overhead to request processing — may impact latency for high-frequency queries

Metrics are exposed on a separate HTTP endpoint — requires additional network configuration

What makes it unique

vs alternatives

Provides native observability integration with OpenTelemetry and Prometheus, whereas generic MCP servers require custom instrumentation or external monitoring

tool schema discovery and dynamic tool registration

Medium confidence

Solves for

Best for

AI agents that need to discover available tools at runtime

Developers building MCP clients that need to adapt to different server configurations

Teams that want to expose different tools based on Grafana configuration

Requires

MCP client that supports tool discovery (most modern MCP clients)

Grafana instance with configured datasources and features

Limitations

Tool schemas are static after server startup — changes to Grafana configuration require server restart

Tool discovery is limited to tools exposed by the MCP server — no discovery of Grafana UI features

Schema validation is basic — complex parameter constraints may not be expressible in tool schemas

What makes it unique

vs alternatives

Provides automatic tool discovery based on Grafana configuration, whereas static MCP servers require manual tool schema definition and updates

grafana variable resolution and dashboard context propagation

Medium confidence

Solves for

Best for

Incident investigation workflows that need to execute dashboard queries with current context

AI agents analyzing dashboards with complex variable configurations

Automation that needs to programmatically resolve and use dashboard variables

Requires

Dashboard ID with variable definitions

Grafana API key with dashboard read permissions

Limitations

Variable resolution is limited to variables defined in the dashboard — no cross-dashboard variable references

Dynamic variable values (from queries) require executing the variable query — adds latency

Variable cascading (where one variable depends on another) requires sequential resolution

What makes it unique

vs alternatives

Provides automatic variable resolution based on dashboard definitions, whereas generic query tools require manual variable substitution

grafana folder and permission-aware resource navigation

Medium confidence

Solves for

I want to list all folders and their contents that I have access toI need to find a dashboard within a specific folder hierarchyI want to understand the permission structure of Grafana resources

Best for

Large Grafana deployments with complex folder hierarchies and RBAC

Teams that need to enforce permission boundaries in AI-driven workflows

Automation that needs to respect Grafana's access control model

Requires

Grafana instance with folder structure configured

API key with folder and resource read permissions

Limitations

Folder navigation is limited to folders the user has read access to — no discovery of restricted folders

Permission inheritance is complex — folder permissions may be overridden at resource level

Folder hierarchy depth may be large — listing all folders could be slow for deeply nested structures

What makes it unique

vs alternatives

Provides permission-aware resource discovery that enforces Grafana's access control, whereas generic API clients require manual permission filtering

pyroscope profiling data querying and trace analysis

Medium confidence

Solves for

Best for

Performance engineering teams that need programmatic access to profiling data

AI agents analyzing application performance for optimization recommendations

Incident response workflows that need to correlate performance issues with metrics

Requires

Grafana instance with Pyroscope datasource configured

Pyroscope instance with profiling data collection enabled

API key with Pyroscope query permissions

Limitations

Profiling data is only available if Pyroscope is configured as a datasource in Grafana

Profile data can be large — result size limits may truncate detailed profiling information

Flame graph generation requires client-side rendering — MCP server returns raw profiling data

What makes it unique

vs alternatives

Provides native Pyroscope integration with profiling data querying, whereas generic profiling tools require separate integrations and lack Grafana context

grafana user and organization management querying

Medium confidence

Solves for

I want to see all users in a Grafana organizationI need to understand a user's role and permissionsI want to list all organizations in a Grafana instance

Best for

Grafana administrators that need programmatic access to user and organization data

Automation workflows that need to manage Grafana users and permissions

AI agents that need to understand organizational structure for access control

Requires

Grafana instance with admin API enabled

API key with admin permissions (for user and organization queries)

Limitations

User and organization management (create/update/delete) is not supported — read-only access only

User data is limited to information exposed by Grafana API — no custom user attributes

Organization membership queries are limited to organizations the user has access to

What makes it unique

vs alternatives

Provides native Grafana admin integration with user and organization querying, whereas third-party admin tools require separate integrations and lack Grafana context

context window optimization and token usage tracking

Medium confidence

Solves for

Best for

Teams using expensive LLM APIs that charge per token

AI agents with limited context windows that need to work with large datasets

Operators that need to track and optimize token usage

Requires

OpenTelemetry setup (optional, for token tracking)

LLM tokenizer configuration (for accurate token estimation)

Limitations

Token estimation is approximate — actual token usage depends on LLM tokenizer

Result truncation may lose important data — intelligent summarization requires domain knowledge

Context window optimization is not automatic — requires explicit tool invocation

What makes it unique

vs alternatives

Provides built-in context window optimization and token tracking, whereas generic MCP servers require manual context management and external token counting tools

dashboard discovery and metadata retrieval with search filtering

Medium confidence

Solves for

Best for

AI agents that need to discover relevant dashboards before executing queries

Teams with large numbers of dashboards (100+) that need programmatic discovery

Automation workflows that need to reference dashboard definitions

Requires

Grafana API key or service account token with dashboard read permissions

Grafana instance with at least one dashboard

Limitations

Search is limited to dashboard name, tags, and folder — does not search panel content or queries

Dashboard definitions can be large (100KB+) for complex dashboards with many panels, impacting token usage in LLM context

Folder hierarchy is flattened in search results — no tree-based folder navigation

What makes it unique

vs alternatives

Provides structured access to full dashboard definitions including panel queries and datasource references, whereas generic Grafana API clients require manual parsing of dashboard JSON

panel data extraction and visualization query execution

Medium confidence

Solves for

Best for

Incident investigation workflows that need to extract data from specific dashboard panels

AI agents analyzing dashboard data to generate insights or alerts

Automation that needs to programmatically access panel data for further processing

Requires

Dashboard ID and panel ID

Grafana API key with dashboard and datasource query permissions

Time range specification (relative or absolute)

Limitations

Panel data extraction is limited to panels with queryable datasources — visualization-only panels (text, static content) return no data

Query execution inherits datasource timeout and result size limits — very large result sets may be truncated

Panel variables must be resolved before query execution — dynamic variable resolution requires additional context

What makes it unique

vs alternatives

Provides direct access to panel data through the MCP interface, whereas generic Grafana API clients require manual dashboard parsing and query reconstruction

prometheus-native metric querying with promql support

Medium confidence

Solves for

Best for

Teams using Prometheus as their primary metrics backend

AI agents that need to execute complex PromQL queries for analysis

Incident response workflows that need rapid metric exploration

Requires

Grafana instance with Prometheus datasource configured

Prometheus instance accessible from MCP server

API key with Prometheus query permissions

Limitations

PromQL query validation is delegated to Prometheus — invalid queries fail at execution time, not at tool invocation

Metric cardinality limits are inherited from Prometheus configuration — high-cardinality queries may timeout

Label value lookups are limited to labels present in the Prometheus instance — no cross-datasource label discovery

What makes it unique

vs alternatives

Provides native PromQL support with metric completion and label discovery, whereas generic Grafana datasource tools require users to construct PromQL manually

loki log querying with logql and log stream filtering

Medium confidence

Solves for

I want to search logs for a specific error message or patternI need to retrieve logs from a specific service or pod over a time rangeI want to discover available log labels and streams in Loki

Best for

Teams using Loki for log aggregation and analysis

Incident investigation workflows that need to correlate logs with metrics

AI agents analyzing logs to identify root causes or patterns

Requires

Grafana instance with Loki datasource configured

Loki instance accessible from MCP server

API key with Loki query permissions

Limitations

LogQL query validation is delegated to Loki — invalid queries fail at execution time

Log result size is limited by Loki max_cache_freshness_per_query — very large log volumes may be truncated

Label discovery is limited to labels present in Loki — no cross-datasource label discovery

What makes it unique

vs alternatives

Provides native LogQL support with label discovery and log stream filtering, whereas generic log query tools require users to construct LogQL manually or use simple text search

alert rule management and alert state querying

Medium confidence

Solves for

I want to see all active alerts and their current statusI need to understand the conditions and thresholds of a specific alert ruleI want to retrieve the history of a specific alert over a time range

Best for

Incident response teams that need programmatic access to alert state

AI agents that need to correlate alerts with metrics and logs

Automation workflows that need to query alert status for decision-making

Requires

Grafana instance with unified alerting enabled

API key with alert rule read permissions

Limitations

Alert rule management (create/update/delete) is not supported — read-only access only

Alert history is limited by Grafana's retention policy — older alerts may not be available

Alert state is point-in-time — no streaming or real-time alert notifications through MCP

What makes it unique

vs alternatives

Provides native Grafana alerting integration with support for unified alerting rules, whereas third-party alert tools require separate integrations for each alerting system

annotation creation and retrieval with time-series correlation

Medium confidence

Solves for

Best for

Incident response workflows that need to mark events for correlation analysis

Automation that needs to record deployment or configuration change events

AI agents analyzing time-series data and needing to correlate with external events

Requires

Grafana instance with annotation storage enabled

API key with annotation write permissions (for creation) or read permissions (for retrieval)

Limitations

Annotation creation requires write permissions — read-only API keys cannot create annotations

Annotations are dashboard-specific or global — no cross-dashboard annotation queries

Annotation storage is limited by Grafana's retention policy — old annotations may be deleted

What makes it unique

vs alternatives

Provides native Grafana annotation integration with time-series correlation, whereas external event tracking systems require separate integrations and lack dashboard visualization

oncall incident management and escalation policy querying

Medium confidence

Solves for

I want to see who is currently on-call for a specific serviceI need to retrieve the current incident status and escalation chainI want to understand the on-call schedule and escalation policies

Best for

Incident response teams that need programmatic access to on-call information

AI agents that need to route incidents to the correct on-call engineer

Automation workflows that need to query on-call status for decision-making

Requires

Grafana Cloud or self-hosted OnCall instance

OnCall API key with read permissions

OnCall integration configured in Grafana

Limitations

OnCall integration requires Grafana Cloud or self-hosted OnCall instance — not available in open-source Grafana

Incident creation/update is not supported — read-only access only

On-call schedule queries are point-in-time — no future schedule predictions

What makes it unique

vs alternatives

Provides native OnCall integration with incident and escalation policy querying, whereas third-party incident tools require separate integrations and lack Grafana context

multi-organization and multi-tenant session management

Medium confidence

Solves for

Best for

Multi-tenant SaaS platforms that need to serve multiple Grafana instances

Enterprise deployments with multiple Grafana organizations

Managed service providers offering Grafana access to multiple customers

Requires

HTTP-based transport (SSE or streamable-http)

Per-request session configuration (Grafana URL, API key, organization ID)

Grafana instances with API access for each tenant

Limitations

Session management is HTTP-only — stdio transport does not support multi-tenant configuration

Session state is per-request — no cross-request session persistence (stateless design)

Organization context is inherited from Grafana API key permissions — no additional RBAC enforcement at MCP layer

What makes it unique

vs alternatives

Provides stateless multi-tenant support through session management, whereas single-tenant MCP servers require separate deployments for each Grafana instance

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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Grafana MCP Server

Capabilities17 decomposed

mcp protocol server with multi-transport bridging

datasource-agnostic query execution with multi-provider support

opentelemetry tracing and prometheus metrics observability

tool schema discovery and dynamic tool registration

grafana variable resolution and dashboard context propagation

grafana folder and permission-aware resource navigation

pyroscope profiling data querying and trace analysis

grafana user and organization management querying

context window optimization and token usage tracking

dashboard discovery and metadata retrieval with search filtering

panel data extraction and visualization query execution

prometheus-native metric querying with promql support

loki log querying with logql and log stream filtering

alert rule management and alert state querying

annotation creation and retrieval with time-series correlation

oncall incident management and escalation policy querying

multi-organization and multi-tenant session management

Related Artifactssharing capabilities

inspector

Teradata

Higress MCP Server Hosting

llm-analysis-assistant

mcp-use

Kubernetes

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

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Get the weekly brief

Data Sources

Grafana MCP Server

Capabilities17 decomposed

mcp protocol server with multi-transport bridging

datasource-agnostic query execution with multi-provider support

opentelemetry tracing and prometheus metrics observability

tool schema discovery and dynamic tool registration

grafana variable resolution and dashboard context propagation

grafana folder and permission-aware resource navigation

pyroscope profiling data querying and trace analysis

grafana user and organization management querying

context window optimization and token usage tracking

dashboard discovery and metadata retrieval with search filtering

panel data extraction and visualization query execution

prometheus-native metric querying with promql support

loki log querying with logql and log stream filtering

alert rule management and alert state querying

annotation creation and retrieval with time-series correlation

oncall incident management and escalation policy querying

multi-organization and multi-tenant session management

Related Artifactssharing capabilities

inspector

Teradata

Higress MCP Server Hosting

llm-analysis-assistant

mcp-use

Kubernetes

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Grafana MCP Server

Are you the builder of Grafana MCP Server?

Get the weekly brief

Data Sources