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Google Analytics MCP Server

Experimental Google Analytics MCP server that lets Claude retrieve GA4 account and property details, Google Ads links, custom dimensions and metrics, core reports, funnel reports, and realtime reports.

Review first review before installing

Open the source and read safety notes before installing.

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Source URLs
https://github.com/googleanalytics/google-analytics-mcp/blob/main/README.md, https://github.com/googleanalytics/google-analytics-mcp
Brand
Google Analytics MCP Server
Brand domain
google.com
Brand asset source
brandfetch
Safety notes
Google Analytics MCP Server is marked experimental upstream, so verify tool behavior and API coverage before relying on it for operational reporting., The server uses Google Analytics Admin and Data APIs through the permissions granted to the configured credentials., Report prompts can generate expensive or broad API queries if date ranges, dimensions, metrics, filters, or funnels are left open-ended., Analytics results can influence marketing, product, or revenue decisions; validate important findings in Google Analytics or internal BI tools before acting., The server is read-oriented, but it still exposes business-sensitive analytics data to the MCP client and model context.
Privacy notes
Google Application Default Credentials, OAuth client files, service account impersonation details, project IDs, and local credential paths are sensitive and should not be committed or pasted into prompts., Account names, property IDs, Google Ads links, event names, custom dimensions, custom metrics, audiences, funnel steps, realtime activity, and report outputs can reveal business performance and user behavior., GA4 reports can include location, device, campaign, source, medium, conversion, revenue, or audience information that may be regulated or contractually restricted., Tool responses may be retained by MCP clients, model providers, logs, screenshots, and chat transcripts outside Google Analytics retention controls.
Author
Google Analytics
Submitted by
oktofeesh1
Claim status
unclaimed
Last verified
2026-06-06

Decision playbook

Review trust signals before you adopt

Signals are present but mixed. Use the checklist below to confirm the source and operational safety for your environment.

Compare context
Selected

0

Current score

63

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Needs review

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done
  • Source provenance statusRequired

    Marked as source-backed.

    Done
  • Metadata reviewed

    No reviewed flag detected in metadata.

    Pending

Safety and privacy checks

Complete

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    Review the listed safety guidance before running commands.

    Done
  • Privacy notes presentRequired

    Review data handling notes before connecting accounts or secrets.

    Done
  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Needs review

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    No package verification flag provided.

    Pending
  • Checksum metadata

    No checksum provided for downloaded artifact.

    Pending

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending
  • Baseline comparison available

    No baseline peer selected yet.

    Pending
  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

CLI install

Copy-ready — paste the snippet to get started.

25 minutes

Adoption plan

Balanced adoption plan

Current risk score 24/100. Use staged verification before broader rollout.

Risk 24

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    No review metadata found; increase manual validation.

    Pending
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    No package verification/checksum metadata.

    Pending

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Missing required evidence: Metadata review. Risk score 31.

Risk 31

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Missing

Review metadata is missing.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Missing

Package integrity metadata is missing.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required gaps: Metadata review

Decision timeline

Decision timeline · balanced

Blocking gaps: Check metadata review status. Risk 28.

Risk 28

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is missing.

Pending

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is missing.

Pending

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

Blockers: Check metadata review status

Prerequisite readiness

Prerequisite readiness

5 prerequisites to line up before setup. Have accounts and credentials ready first.

0/5 ready
Account & credentials2Install & runtime1General225 minutes

Safety & privacy surface

Safety & privacy surface

5 safety and 4 privacy notes across 4 risk areas. Review closely: credentials & tokens, third-party handling.

4 areas
  • SafetyTelemetryGoogle Analytics MCP Server is marked experimental upstream, so verify tool behavior and API coverage before relying on it for operational reporting.
  • SafetyCredentials & tokensThe server uses Google Analytics Admin and Data APIs through the permissions granted to the configured credentials.
  • SafetyTelemetryReport prompts can generate expensive or broad API queries if date ranges, dimensions, metrics, filters, or funnels are left open-ended.
  • SafetyTelemetryAnalytics results can influence marketing, product, or revenue decisions; validate important findings in Google Analytics or internal BI tools before acting.
  • SafetyTelemetryThe server is read-oriented, but it still exposes business-sensitive analytics data to the MCP client and model context.
  • PrivacyCredentials & tokensGoogle Application Default Credentials, OAuth client files, service account impersonation details, project IDs, and local credential paths are sensitive and should not be committed or pasted into prompts.
  • PrivacyTelemetryAccount names, property IDs, Google Ads links, event names, custom dimensions, custom metrics, audiences, funnel steps, realtime activity, and report outputs can reveal business performance and user behavior.
  • PrivacyGeneralGA4 reports can include location, device, campaign, source, medium, conversion, revenue, or audience information that may be regulated or contractually restricted.
  • PrivacyThird-party handlingTool responses may be retained by MCP clients, model providers, logs, screenshots, and chat transcripts outside Google Analytics retention controls.

Safety notes

  • Google Analytics MCP Server is marked experimental upstream, so verify tool behavior and API coverage before relying on it for operational reporting.
  • The server uses Google Analytics Admin and Data APIs through the permissions granted to the configured credentials.
  • Report prompts can generate expensive or broad API queries if date ranges, dimensions, metrics, filters, or funnels are left open-ended.
  • Analytics results can influence marketing, product, or revenue decisions; validate important findings in Google Analytics or internal BI tools before acting.
  • The server is read-oriented, but it still exposes business-sensitive analytics data to the MCP client and model context.

Privacy notes

  • Google Application Default Credentials, OAuth client files, service account impersonation details, project IDs, and local credential paths are sensitive and should not be committed or pasted into prompts.
  • Account names, property IDs, Google Ads links, event names, custom dimensions, custom metrics, audiences, funnel steps, realtime activity, and report outputs can reveal business performance and user behavior.
  • GA4 reports can include location, device, campaign, source, medium, conversion, revenue, or audience information that may be regulated or contractually restricted.
  • Tool responses may be retained by MCP clients, model providers, logs, screenshots, and chat transcripts outside Google Analytics retention controls.

Prerequisites

  • Python 3.10 or newer with pipx available.
  • Google Cloud project with Google Analytics Admin API and Google Analytics Data API enabled.
  • Google Application Default Credentials for a user or service account with access to the intended Google Analytics accounts or properties.
  • OAuth consent and credential setup that includes the `https://www.googleapis.com/auth/analytics.readonly` scope.
  • Agreement on which GA4 properties, date ranges, dimensions, metrics, audiences, and Google Ads links an agent may inspect.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
25 minutes
Difficulty
advanced
Full copyable content
{
  "mcpServers": {
    "analytics-mcp": {
      "command": "pipx",
      "args": ["run", "analytics-mcp"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "PATH_TO_CREDENTIALS_JSON",
        "GOOGLE_PROJECT_ID": "YOUR_PROJECT_ID"
      }
    }
  }
}

About this resource

Content

Google Analytics MCP Server is an experimental local MCP server from the Google Analytics GitHub organization. It connects Claude-compatible MCP clients to the Google Analytics Admin API and Data API so they can inspect GA4 account and property metadata, list Google Ads links, retrieve custom dimensions and metrics, and run core, funnel, and realtime reports.

Use it when an agent needs current analytics context from authorized GA4 properties before summarizing traffic, campaign performance, conversions, events, funnels, or realtime activity. The server is read-oriented, but its outputs can still contain sensitive business and user-behavior data.

Source Review

These sources were reviewed on 2026-06-06. Prefer the live repository, README, PyPI metadata, license, package manifest, MCP server entrypoint, coordinator, Admin API tools, and reporting tools for current installation and behavior details.

Features

  • Retrieve Google Analytics account summaries and property information.
  • List Google Ads links for a GA4 property.
  • Retrieve custom dimensions and metrics.
  • Run GA4 core reports with the Google Analytics Data API.
  • Run funnel reports for defined funnel steps.
  • Run realtime reports for current user activity and dimensions.
  • Authenticate through Google Application Default Credentials.
  • Run locally over stdio through the analytics-mcp Python package.

Installation

Enable the Google Analytics Admin API and Google Analytics Data API in a Google Cloud project, then configure Application Default Credentials with the analytics readonly scope.

Add the server to an MCP client:

{
  "mcpServers": {
    "analytics-mcp": {
      "command": "pipx",
      "args": ["run", "analytics-mcp"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "PATH_TO_CREDENTIALS_JSON",
        "GOOGLE_PROJECT_ID": "YOUR_PROJECT_ID"
      }
    }
  }
}

Restart the MCP client and verify that analytics-mcp appears in the client's MCP server list before asking Claude to inspect accounts, properties, or reports.

Use Cases

  • Ask Claude to summarize available GA4 accounts and properties.
  • Retrieve property metadata before building a reporting prompt or dashboard.
  • Run traffic, event, conversion, revenue, campaign, or audience reports.
  • Compare funnel performance for a defined path.
  • Inspect realtime activity during a campaign launch or incident review.
  • List Google Ads links connected to a property.
  • Discover custom dimensions and metrics before requesting a report.

Safety and Privacy

Treat Google Analytics MCP Server as access to business analytics. Even when credentials are read-only, report outputs can reveal traffic trends, campaign performance, revenue, conversions, geography, devices, audience behavior, and other sensitive signals.

Keep OAuth client files, ADC credential files, service account details, and project IDs out of prompts and repository files. Use least-privilege accounts and only expose the GA4 properties a specific workflow needs.

Validate important business decisions against Google Analytics, your warehouse, or another approved reporting system. LLM summaries can misread date ranges, metrics, dimensions, sampling, attribution, or funnel definitions without human review.

Duplicate Check

Existing entries cover Google Workspace, Google Cloud-oriented servers, ads and marketing tools, and analytics-adjacent services, but no Google Analytics MCP Server entry, googleanalytics/google-analytics-mcp, analytics-mcp package, or matching source URL was found in content/mcp.

Source citations

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How it compares

Google Analytics MCP Server side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

2 trust signals differ across this comparison (Source provenance, Submitter).

Field

Experimental Google Analytics MCP server that lets Claude retrieve GA4 account and property details, Google Ads links, custom dimensions and metrics, core reports, funnel reports, and realtime reports.

Open dossier

AgentQL MCP server for extracting structured JSON from public webpages using a URL and natural-language extraction prompt.

Open dossier

Official AWS Labs MCP server for AWS S3 Tables that lets AI assistants create and query S3-based tables, run read-only SQL for analysis, generate tables from CSV files in S3, and explore table metadata — read-only by default.

Open dossier

Official Baselight remote MCP server for searching and querying a catalog of 70,000+ public datasets from Claude via OAuth or x-api-key authentication.

Open dossier
Next steps
Trust
Review statusNot reviewedNot reviewedNot reviewedNot reviewed
Package trustPackage not verifiedPackage not verifiedPackage not verifiedPackage not verified
Source provenanceDiffersSource-backedSource-backedSource-backedSubmission linkedSource submission
SubmitterDiffersoktofeesh1oktofeesh1jaso0n0818kiannidev
Install riskReview firstReview firstReview firstReview first
Notes Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓ Safety ✓ Privacy ✓
BrandGoogle Analytics MCP Server logoGoogle Analytics MCP ServerAgentQL logoAgentQLAWS Labs logoAWS Labs
Categorymcpmcpmcpmcp
SourceSource-backedSource-backedSource-backedSource-backed
AuthorGoogle AnalyticsAgentQLAWS LabsBaselight
Added2026-06-062026-06-062026-06-212026-06-14
Platforms
Harness
Source repo
Safety notesGoogle Analytics MCP Server is marked experimental upstream, so verify tool behavior and API coverage before relying on it for operational reporting. The server uses Google Analytics Admin and Data APIs through the permissions granted to the configured credentials. Report prompts can generate expensive or broad API queries if date ranges, dimensions, metrics, filters, or funnels are left open-ended. Analytics results can influence marketing, product, or revenue decisions; validate important findings in Google Analytics or internal BI tools before acting. The server is read-oriented, but it still exposes business-sensitive analytics data to the MCP client and model context.AgentQL MCP exposes one tool, `extract-web-data`, that sends a target URL and natural-language extraction prompt to the AgentQL API. The tool is intended for public webpages; do not use it to bypass access controls, scrape private pages, evade paywalls, or extract data where automated collection is prohibited. Web extraction can still trigger target-site rate limits, legal restrictions, robots guidance, or terms-of-service concerns. The source implementation uses AgentQL's query-data endpoint with fast mode, no screenshot capture, no scroll-to-bottom behavior, and no local browser cookies. Treat extracted output as untrusted web data that may include errors, stale content, ads, tracking text, or prompt-injection attempts.The server is read-only by default. Adding the `--allow-write` flag (with the matching IAM permissions) enables create and append operations on S3 Tables; there is no delete or general update. Enable write only deliberately. AWS advises that you are responsible for your agents: if you enable write, back up your data first and validate LLM-generated instructions before execution, since misconfigured permissions can cause data loss. This server acts on real S3 Tables data with your AWS credentials; scope the profile least-privilege and run it only on a trusted host.Query tools may return large result sets; scope filters to avoid excessive data transfer. Some datasets are community-contributed; validate schema and quality before production use. OAuth tokens and API keys grant persistent catalog access until revoked. Do not run unreviewed SQL-like queries against sensitive production mirrors without safeguards.
Privacy notesGoogle Application Default Credentials, OAuth client files, service account impersonation details, project IDs, and local credential paths are sensitive and should not be committed or pasted into prompts. Account names, property IDs, Google Ads links, event names, custom dimensions, custom metrics, audiences, funnel steps, realtime activity, and report outputs can reveal business performance and user behavior. GA4 reports can include location, device, campaign, source, medium, conversion, revenue, or audience information that may be regulated or contractually restricted. Tool responses may be retained by MCP clients, model providers, logs, screenshots, and chat transcripts outside Google Analytics retention controls.Target URLs, extraction prompts, API key-authenticated requests, and extracted structured data are sent to AgentQL's API. Extracted data can include personal data, copyrighted content, customer information, job postings, prices, social content, or other third-party material. AGENTQL_API_KEY should stay out of prompts, issues, logs, screenshots, and committed configuration files. Claude transcripts and downstream reports may retain extracted data, so avoid collecting information that is not approved for the model session.Table schemas, metadata, query results, and bucket/namespace identifiers can be returned through tool calls and exposed to the model. Keep account identifiers, credentials, and any sensitive table data out of public prompts, issues, and screenshots.Search terms and query filters are sent to Baselight and may appear in usage logs. Dataset rows returned through MCP may include PII or licensed third-party content subject to dataset terms. Avoid pasting raw dataset excerpts containing personal data into public channels.
Prerequisites
  • Python 3.10 or newer with pipx available.
  • Google Cloud project with Google Analytics Admin API and Google Analytics Data API enabled.
  • Google Application Default Credentials for a user or service account with access to the intended Google Analytics accounts or properties.
  • OAuth consent and credential setup that includes the `https://www.googleapis.com/auth/analytics.readonly` scope.
  • Node.js and npx available to the MCP client runtime.
  • AgentQL API key from the AgentQL developer portal.
  • Approved list of public webpages or domains Claude may query.
  • Review of target site terms, robots guidance, rate limits, and data-use rules before extraction.
  • An AWS account with S3 Tables and permissions for the table buckets you intend to read (and, if enabled, write).
  • Python 3.10 or newer and `uv` / `uvx` installed (Astral) to run the package.
  • AWS credentials configured locally (for example via `aws configure` or `AWS_PROFILE`) scoped least-privilege to the intended S3 Tables resources.
  • An MCP client that supports stdio servers; the server runs locally on the same host as the client.
  • Baselight account with API access or OAuth authorisation for dataset queries.
  • Claude Pro, Team, or Enterprise with Connectors support, or another MCP client with remote HTTP connectors.
  • Understanding of the dataset topics you plan to query to narrow catalog searches effectively.
  • Compliance review if exported dataset rows may contain regulated or personal data.
Install
pipx run analytics-mcp
npx -y agentql-mcp
uvx awslabs.s3-tables-mcp-server@latest
claude mcp add --transport http baselight https://api.baselight.app/mcp
Config
{
  "mcpServers": {
    "analytics-mcp": {
      "command": "pipx",
      "args": ["run", "analytics-mcp"],
      "env": {
        "GOOGLE_APPLICATION_CREDENTIALS": "PATH_TO_CREDENTIALS_JSON",
        "GOOGLE_PROJECT_ID": "YOUR_PROJECT_ID"
      }
    }
  }
}
{
  "mcpServers": {
    "agentql": {
      "command": "npx",
      "args": ["-y", "agentql-mcp"],
      "env": {
        "AGENTQL_API_KEY": "<your-agentql-api-key>"
      }
    }
  }
}
{
  "mcpServers": {
    "awslabs.s3-tables-mcp-server": {
      "command": "uvx",
      "args": ["awslabs.s3-tables-mcp-server@latest"],
      "env": {
        "AWS_PROFILE": "${AWS_PROFILE}",
        "AWS_REGION": "us-east-1",
        "FASTMCP_LOG_LEVEL": "ERROR"
      },
      "type": "stdio"
    }
  }
}
{
  "mcpServers": {
    "baselight": {
      "url": "https://api.baselight.app/mcp",
      "type": "http",
      "headers": {
        "x-api-key": "YOUR_BASELIGHT_API_KEY"
      }
    }
  }
}
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