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.
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.
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.
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.
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.
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.
Show that Google Analytics MCP Server is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/mcp/google-analytics-mcp-server)
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).
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.
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.
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.
✓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.
✓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 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.
✓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