| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 17 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
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.
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
+9 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
Grafana MCP Server scores higher at 60/100 vs Supabase at 42/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities