What can @google-cloud/observability-mcp do?

gcp cloud logging query and retrieval via mcp, gcp cloud monitoring metrics query and analysis via mcp, gcp cloud trace distributed tracing data retrieval via mcp, gcp cloud profiler data retrieval and analysis via mcp, gcp error reporting incident retrieval and analysis via mcp, gcp cloud audit logs retrieval and compliance analysis via mcp, mcp protocol server implementation for gcp observability

@google-cloud/observability-mcp

MCP ServerFree

MCP Server for GCP environment for interacting with various Observability APIs.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

gcp cloud logging query and retrieval via mcp

Medium confidence

Exposes Google Cloud Logging APIs through MCP protocol, enabling Claude and other LLM clients to query, filter, and retrieve logs from GCP projects using natural language or structured queries. Implements MCP resource and tool abstractions that translate client requests into Cloud Logging API calls, handling authentication via Application Default Credentials or service account keys.

Solves for

Query application logs across GCP projects without leaving ClaudeRetrieve error logs and stack traces for debugging production issuesFilter logs by severity, timestamp, resource type, or custom labelsAnalyze log patterns to identify root causes of system failures

Best for

DevOps engineers debugging GCP-hosted applications in Claude

SREs building AI-assisted incident response workflows

Teams using Claude as a unified interface for GCP observability

Requires

Google Cloud project with Cloud Logging API enabled

GCP authentication (Application Default Credentials or service account JSON key)

Node.js 18+ runtime for MCP server

Limitations

Query performance depends on GCP Cloud Logging API rate limits (default 1000 requests/min per project)

Large result sets (>10k logs) may require pagination handling in client

No built-in log aggregation across multiple GCP projects in single query

What makes it unique

Bridges GCP Cloud Logging directly into Claude's tool ecosystem via MCP protocol, eliminating context switching between GCP console and LLM; uses MCP resource abstraction to expose logs as queryable entities rather than simple API wrappers

vs alternatives

Tighter integration than generic GCP SDKs because it's purpose-built for MCP clients, enabling Claude to reason about logs natively without custom wrapper code

gcp cloud monitoring metrics query and analysis via mcp

Medium confidence

Exposes Google Cloud Monitoring (Stackdriver) APIs through MCP, allowing LLM clients to query time-series metrics, retrieve metric metadata, and analyze performance data. Implements MCP tool bindings that translate metric queries into Cloud Monitoring API calls, supporting metric filtering by resource type, labels, and time windows.

Solves for

Query CPU, memory, and disk metrics for GCP VMs and servicesRetrieve custom application metrics for performance analysisFetch metric metadata to understand available observability signalsAnalyze metric trends over time to identify performance degradation

Best for

Performance engineers analyzing GCP infrastructure metrics in Claude

SREs building AI-assisted capacity planning workflows

Developers investigating resource utilization issues during incidents

Requires

Google Cloud project with Cloud Monitoring API enabled

GCP authentication (Application Default Credentials or service account key)

Node.js 18+ runtime

Limitations

Metric query latency depends on time window size and cardinality (larger windows = slower queries)

No built-in metric forecasting or anomaly detection — requires external analysis

Custom metrics require pre-existing metric definitions in Cloud Monitoring

What makes it unique

Integrates GCP Cloud Monitoring as a queryable tool within Claude's reasoning loop, using MCP's structured tool protocol to expose metric queries as first-class operations rather than generic API calls

vs alternatives

More direct than using GCP CLI or console because Claude can reason about metric results inline and chain queries together; avoids context loss from switching between tools

gcp cloud trace distributed tracing data retrieval via mcp

Medium confidence

Exposes Google Cloud Trace APIs through MCP, enabling LLM clients to retrieve distributed trace data, analyze request flows, and identify latency bottlenecks. Implements MCP tool bindings that query Cloud Trace for spans, traces, and trace metadata, supporting filtering by service, trace ID, and time range.

Solves for

Retrieve distributed traces for a specific request to understand call flowIdentify which service or span is causing request latencyAnalyze trace data to find performance bottlenecks in microservicesCorrelate traces with logs and metrics for holistic incident analysis

Best for

Microservices engineers debugging request latency in Claude

SREs investigating cross-service performance issues

Teams building AI-assisted root cause analysis for distributed systems

Requires

Google Cloud project with Cloud Trace API enabled

GCP authentication (Application Default Credentials or service account key)

Services instrumented with Cloud Trace SDKs (OpenTelemetry, Jaeger, etc.)

Limitations

Trace data retention depends on GCP project settings (default 30 days)

Trace queries require trace ID or service name; no full-text search across span data

Large traces (>1000 spans) may be truncated or require pagination

What makes it unique

Brings GCP Cloud Trace into Claude's reasoning context via MCP, allowing the LLM to traverse distributed traces and correlate span data without manual console navigation

vs alternatives

Enables Claude to analyze trace data programmatically and reason about cross-service latency patterns, whereas traditional trace viewers require manual inspection

gcp cloud profiler data retrieval and analysis via mcp

Medium confidence

Exposes Google Cloud Profiler APIs through MCP, allowing LLM clients to retrieve CPU, memory, and allocation profiles for GCP services. Implements MCP tool bindings that query Cloud Profiler for profile data, supporting filtering by service, deployment, and time range, with profile parsing to extract hotspots and resource usage patterns.

Solves for

Retrieve CPU profiles to identify performance hotspots in production codeAnalyze memory allocation profiles to find memory leaksCompare profiles across deployments to detect performance regressionsIdentify resource-intensive functions for optimization

Best for

Performance engineers analyzing production code hotspots in Claude

Backend developers investigating memory leaks in GCP services

Teams building AI-assisted performance optimization workflows

Requires

Google Cloud project with Cloud Profiler API enabled

GCP authentication (Application Default Credentials or service account key)

Cloud Profiler agent installed and running on target services

Limitations

Profiler data collection requires Cloud Profiler agent running on services

Profile granularity depends on sampling rate (default 100 samples/sec for CPU)

Memory profiles may not capture all allocations due to sampling

What makes it unique

Integrates GCP Cloud Profiler as a queryable tool in Claude, enabling the LLM to retrieve and analyze production profiles without manual GCP console access; parses profile data to extract actionable hotspot information

vs alternatives

Allows Claude to reason about performance profiles and suggest optimizations based on actual production data, whereas generic profiler tools require manual interpretation

gcp error reporting incident retrieval and analysis via mcp

Medium confidence

Exposes Google Cloud Error Reporting APIs through MCP, enabling LLM clients to retrieve error groups, error details, and incident summaries. Implements MCP tool bindings that query Error Reporting for error events, supporting filtering by service, error message, and time range, with automatic grouping and deduplication of similar errors.

Solves for

Retrieve error groups to understand recurring issues in productionGet error details including stack traces and affected usersAnalyze error trends to identify emerging issuesCorrelate errors with logs and traces for root cause analysis

Best for

Support engineers investigating customer-reported errors in Claude

Backend developers analyzing production error patterns

Teams building AI-assisted incident triage workflows

Requires

Google Cloud project with Error Reporting API enabled

GCP authentication (Application Default Credentials or service account key)

Applications configured to report errors to Cloud Error Reporting

Limitations

Error grouping algorithm is GCP-managed; no custom grouping rules

Error Reporting requires explicit error reporting from applications (not automatic)

Error data retention depends on GCP project settings (default 30 days)

What makes it unique

Brings GCP Error Reporting into Claude's incident analysis workflow via MCP, allowing the LLM to retrieve and correlate error data with other observability signals without context switching

vs alternatives

Enables Claude to perform automated error triage and root cause analysis by combining error data with logs and traces, whereas manual error reporting review is time-consuming

gcp cloud audit logs retrieval and compliance analysis via mcp

Medium confidence

Exposes Google Cloud Audit Logs APIs through MCP, enabling LLM clients to retrieve audit events, analyze access patterns, and investigate security/compliance events. Implements MCP tool bindings that query Cloud Audit Logs for admin activity, data access, and system events, supporting filtering by principal, resource, and action type.

Solves for

Retrieve audit logs to investigate who accessed a specific resourceAnalyze access patterns to identify unauthorized or suspicious activityTrack configuration changes for compliance auditingCorrelate audit events with security incidents for forensic analysis

Best for

Security engineers investigating access logs in Claude

Compliance officers analyzing audit trails for regulatory requirements

Teams building AI-assisted security incident response workflows

Requires

Google Cloud project with Cloud Audit Logs enabled

GCP authentication (Application Default Credentials or service account key)

Appropriate IAM permissions to read audit logs

Limitations

Admin Activity logs retained for 400 days; Data Access logs for 30 days (configurable)

Audit log queries may return large result sets requiring pagination

No built-in anomaly detection for suspicious access patterns

What makes it unique

Integrates GCP Cloud Audit Logs as a queryable tool in Claude, enabling the LLM to perform security investigations and compliance analysis without manual log console access

vs alternatives

Allows Claude to correlate audit events with other observability data and reason about access patterns, whereas manual audit log review is labor-intensive and error-prone

mcp protocol server implementation for gcp observability

Medium confidence

Implements a complete MCP server that exposes GCP observability APIs as MCP tools and resources, handling protocol negotiation, request/response serialization, and error handling. Uses MCP SDK to define tool schemas, manage client connections, and translate between MCP protocol messages and GCP API calls, with built-in support for streaming responses and long-running operations.

Solves for

Deploy an MCP server that Claude can connect to for GCP observability queriesIntegrate GCP observability into any MCP-compatible client applicationStandardize access to multiple GCP observability APIs through a single MCP interfaceEnable multi-client access to GCP observability data with centralized authentication

Best for

Teams building Claude-integrated GCP observability workflows

Organizations standardizing on MCP for AI-assisted operations

Developers extending GCP observability with custom MCP clients

Requires

Node.js 18+ runtime

GCP authentication (Application Default Credentials or service account key)

@google-cloud/observability-mcp npm package

Limitations

MCP server runs as a single process; no built-in clustering or high availability

Authentication is per-server instance; no centralized credential management

Tool schemas are static; dynamic schema generation not supported

What makes it unique

Purpose-built MCP server implementation that handles all protocol details and GCP API integration, using MCP SDK abstractions to expose observability APIs as first-class tools rather than generic function calls

vs alternatives

Tighter integration than generic MCP wrappers because it's specifically designed for GCP observability, with pre-built tool schemas and error handling optimized for observability workflows

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

Related Artifactssharing capabilities

Artifacts that share capabilities with @google-cloud/observability-mcp, ranked by overlap. Discovered automatically through the match graph.

MCP Server25

AWS Core

** - Core AWS MCP server providing prompt understanding and server management capabilities.

execution tracing and observability instrumentationaudit logging and compliance tracking

2 shared capabilities

MCP Server30

@mcp-use/cli

The mcp-use CLI is a tool for building and deploying MCP servers with support for ChatGPT Apps, Code Mode, OAuth, Notifications, Sampling, Observability and more.

observability and structured logging

1 shared capability

MCP Server40

mcp

Official MCP Servers for AWS

monitoring, logging, and observability tool access (cloudwatch, cloudtrail, cost explorer)

1 shared capability

MCP Server32

@cloudflare/mcp-server-cloudflare

MCP server for interacting with Cloudflare API

shared mcp infrastructure and observability framework

1 shared capability

MCP Server26

datadog-mcp-server

MCP Server for Datadog API

datadog trace and apm data retrieval via mcp

1 shared capability

MCP Server26

mcp.natoma.ai

** – A Hosted MCP Platform to discover, install, manage and deploy MCP servers by **[Natoma Labs](https://www.natoma.ai)**

mcp server monitoring, logging, and observability integration

1 shared capability

Best For

✓DevOps engineers debugging GCP-hosted applications in Claude
✓SREs building AI-assisted incident response workflows
✓Teams using Claude as a unified interface for GCP observability
✓Performance engineers analyzing GCP infrastructure metrics in Claude
✓SREs building AI-assisted capacity planning workflows
✓Developers investigating resource utilization issues during incidents
✓Microservices engineers debugging request latency in Claude
✓SREs investigating cross-service performance issues

Known Limitations

⚠Query performance depends on GCP Cloud Logging API rate limits (default 1000 requests/min per project)
⚠Large result sets (>10k logs) may require pagination handling in client
⚠No built-in log aggregation across multiple GCP projects in single query
⚠Requires appropriate IAM roles (logging.logEntries.list) on target project
⚠Metric query latency depends on time window size and cardinality (larger windows = slower queries)
⚠No built-in metric forecasting or anomaly detection — requires external analysis

Requirements

Google Cloud project with Cloud Logging API enabledGCP authentication (Application Default Credentials or service account JSON key)Node.js 18+ runtime for MCP serverMCP client compatible with tool/resource protocol (Claude, etc.)Google Cloud project with Cloud Monitoring API enabledGCP authentication (Application Default Credentials or service account key)Node.js 18+ runtimeMCP client with tool support

Input / Output

Accepts: structured query filters (severity, resource type, timestamp range), natural language log search queries, GCP resource identifiers (project ID, log name), metric names and resource filters, time window specifications (start/end timestamps), label-based filtering criteria, aggregation function specifications, trace IDs, service names, time window filters, span name patterns, deployment identifiers, profile types (CPU, memory, allocation, heap), time window specifications, error message patterns, error group identifiers, principal identifiers (users, service accounts), resource names and types, action/method names, MCP protocol messages (tool calls, resource requests), GCP API credentials and configuration

Produces: JSON-formatted log entries with metadata, structured error summaries with stack traces, log statistics and aggregation results, time-series metric data points with timestamps, metric metadata (unit, description, labels), aggregated metric statistics, trace data with span hierarchy and timing, span metadata (service, operation, duration, status), latency analysis summaries, profile data with function call stacks and resource usage, hotspot summaries (top functions by CPU/memory), flame graph data structures, error group summaries with occurrence counts, error event details (stack traces, user info, timestamps), error trend analysis, audit event details (who, what, when, where), access pattern summaries, configuration change history, MCP protocol responses (tool results, resource data), structured observability data from GCP APIs

UnfragileRank

Adoption15%(25% weight)

Quality16%(25% weight)

Ecosystem45%(15% weight)

Match Graph25%(30% weight)

Freshness75%(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

7 capabilities

Visit @google-cloud/observability-mcp→

Package Details

npm

Registry

0.2.3

Version

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MCP Server for GCP environment for interacting with various Observability APIs.

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Capabilities7 decomposed

gcp cloud logging query and retrieval via mcp

Medium confidence

Solves for

Best for

DevOps engineers debugging GCP-hosted applications in Claude

SREs building AI-assisted incident response workflows

Teams using Claude as a unified interface for GCP observability

Requires

Google Cloud project with Cloud Logging API enabled

GCP authentication (Application Default Credentials or service account JSON key)

Node.js 18+ runtime for MCP server

Limitations

Query performance depends on GCP Cloud Logging API rate limits (default 1000 requests/min per project)

Large result sets (>10k logs) may require pagination handling in client

No built-in log aggregation across multiple GCP projects in single query

What makes it unique

vs alternatives

Tighter integration than generic GCP SDKs because it's purpose-built for MCP clients, enabling Claude to reason about logs natively without custom wrapper code

gcp cloud monitoring metrics query and analysis via mcp

Medium confidence

Solves for

Best for

Performance engineers analyzing GCP infrastructure metrics in Claude

SREs building AI-assisted capacity planning workflows

Developers investigating resource utilization issues during incidents

Requires

Google Cloud project with Cloud Monitoring API enabled

GCP authentication (Application Default Credentials or service account key)

Node.js 18+ runtime

Limitations

Metric query latency depends on time window size and cardinality (larger windows = slower queries)

No built-in metric forecasting or anomaly detection — requires external analysis

Custom metrics require pre-existing metric definitions in Cloud Monitoring

What makes it unique

vs alternatives

More direct than using GCP CLI or console because Claude can reason about metric results inline and chain queries together; avoids context loss from switching between tools

gcp cloud trace distributed tracing data retrieval via mcp

Medium confidence

Solves for

Best for

Microservices engineers debugging request latency in Claude

SREs investigating cross-service performance issues

Teams building AI-assisted root cause analysis for distributed systems

Requires

Google Cloud project with Cloud Trace API enabled

GCP authentication (Application Default Credentials or service account key)

Services instrumented with Cloud Trace SDKs (OpenTelemetry, Jaeger, etc.)

Limitations

Trace data retention depends on GCP project settings (default 30 days)

Trace queries require trace ID or service name; no full-text search across span data

Large traces (>1000 spans) may be truncated or require pagination

What makes it unique

Brings GCP Cloud Trace into Claude's reasoning context via MCP, allowing the LLM to traverse distributed traces and correlate span data without manual console navigation

vs alternatives

Enables Claude to analyze trace data programmatically and reason about cross-service latency patterns, whereas traditional trace viewers require manual inspection

gcp cloud profiler data retrieval and analysis via mcp

Medium confidence

Solves for

Best for

Performance engineers analyzing production code hotspots in Claude

Backend developers investigating memory leaks in GCP services

Teams building AI-assisted performance optimization workflows

Requires

Google Cloud project with Cloud Profiler API enabled

GCP authentication (Application Default Credentials or service account key)

Cloud Profiler agent installed and running on target services

Limitations

Profiler data collection requires Cloud Profiler agent running on services

Profile granularity depends on sampling rate (default 100 samples/sec for CPU)

Memory profiles may not capture all allocations due to sampling

What makes it unique

vs alternatives

Allows Claude to reason about performance profiles and suggest optimizations based on actual production data, whereas generic profiler tools require manual interpretation

gcp error reporting incident retrieval and analysis via mcp

Medium confidence

Solves for

Best for

Support engineers investigating customer-reported errors in Claude

Backend developers analyzing production error patterns

Teams building AI-assisted incident triage workflows

Requires

Google Cloud project with Error Reporting API enabled

GCP authentication (Application Default Credentials or service account key)

Applications configured to report errors to Cloud Error Reporting

Limitations

Error grouping algorithm is GCP-managed; no custom grouping rules

Error Reporting requires explicit error reporting from applications (not automatic)

Error data retention depends on GCP project settings (default 30 days)

What makes it unique

Brings GCP Error Reporting into Claude's incident analysis workflow via MCP, allowing the LLM to retrieve and correlate error data with other observability signals without context switching

vs alternatives

Enables Claude to perform automated error triage and root cause analysis by combining error data with logs and traces, whereas manual error reporting review is time-consuming

gcp cloud audit logs retrieval and compliance analysis via mcp

Medium confidence

Solves for

Best for

Security engineers investigating access logs in Claude

Compliance officers analyzing audit trails for regulatory requirements

Teams building AI-assisted security incident response workflows

Requires

Google Cloud project with Cloud Audit Logs enabled

GCP authentication (Application Default Credentials or service account key)

Appropriate IAM permissions to read audit logs

Limitations

Admin Activity logs retained for 400 days; Data Access logs for 30 days (configurable)

Audit log queries may return large result sets requiring pagination

No built-in anomaly detection for suspicious access patterns

What makes it unique

Integrates GCP Cloud Audit Logs as a queryable tool in Claude, enabling the LLM to perform security investigations and compliance analysis without manual log console access

vs alternatives

Allows Claude to correlate audit events with other observability data and reason about access patterns, whereas manual audit log review is labor-intensive and error-prone

mcp protocol server implementation for gcp observability

Medium confidence

Solves for

Best for

Teams building Claude-integrated GCP observability workflows

Organizations standardizing on MCP for AI-assisted operations

Developers extending GCP observability with custom MCP clients

Requires

Node.js 18+ runtime

GCP authentication (Application Default Credentials or service account key)

@google-cloud/observability-mcp npm package

Limitations

MCP server runs as a single process; no built-in clustering or high availability

Authentication is per-server instance; no centralized credential management

Tool schemas are static; dynamic schema generation not supported

What makes it unique

vs alternatives

Tighter integration than generic MCP wrappers because it's specifically designed for GCP observability, with pre-built tool schemas and error handling optimized for observability workflows

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

Alternatives to @google-cloud/observability-mcp

wink-embeddings-sg-100d24Repository

100-dimensional English word embeddings for wink-nlp

Compare →

voyage-ai-provider29API

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@vibe-agent-toolkit/rag-lancedb27Agent

LanceDB implementation of RAG interfaces for vibe-agent-toolkit

Compare →

vectra38Repository

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@google-cloud/observability-mcp

Capabilities7 decomposed

gcp cloud logging query and retrieval via mcp

gcp cloud monitoring metrics query and analysis via mcp

gcp cloud trace distributed tracing data retrieval via mcp

gcp cloud profiler data retrieval and analysis via mcp

gcp error reporting incident retrieval and analysis via mcp

gcp cloud audit logs retrieval and compliance analysis via mcp

mcp protocol server implementation for gcp observability

Related Artifactssharing capabilities

AWS Core

@mcp-use/cli

mcp

@cloudflare/mcp-server-cloudflare

datadog-mcp-server

mcp.natoma.ai

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @google-cloud/observability-mcp

Are you the builder of @google-cloud/observability-mcp?

Get the weekly brief

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@google-cloud/observability-mcp

Capabilities7 decomposed

gcp cloud logging query and retrieval via mcp

gcp cloud monitoring metrics query and analysis via mcp

gcp cloud trace distributed tracing data retrieval via mcp

gcp cloud profiler data retrieval and analysis via mcp

gcp error reporting incident retrieval and analysis via mcp

gcp cloud audit logs retrieval and compliance analysis via mcp

mcp protocol server implementation for gcp observability

Related Artifactssharing capabilities

AWS Core

@mcp-use/cli

mcp

@cloudflare/mcp-server-cloudflare

datadog-mcp-server

mcp.natoma.ai

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to @google-cloud/observability-mcp

Are you the builder of @google-cloud/observability-mcp?

Get the weekly brief

Data Sources