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ga-gsc-analytics

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ga-gsc-analytics

Read-only MCP server exposing Google Analytics 4 and Google Search Console data (analytics.readonly + webmasters.readonly scopes) to Claude Code / Codex.

New here? Follow ONBOARDING.md for a zero-to-working walkthrough (install Claude Code, get credentials, authorize, verify). The rest of this file is reference material.

AI agents: this file doubles as AGENTS.md — start here for setup, then read skills/analyze-ga-gsc/SKILL.md for how to use the tools (access checks, default date ranges, GA4 vs. GSC semantics, presentation handoff format). Never ask a user to paste credentials into chat.

Tools

  • analytics_auth_start / analytics_auth_status — local OAuth flow + status check

  • analytics_list_sources — list GA4 properties and GSC sites available to the authorized account

  • ga_run_report — run a GA4 report

  • gsc_query_search_analytics — query Search Console data

  • analytics_presentation_snapshot — combined GA4 + GSC snapshot for reporting

Related MCP server: ga4-mcp

Setup (per user)

Each user authorizes with their own Google account and keeps their own local token — never share client_secret.json secrets or tokens.json between machines.

  1. Google Cloud OAuth client: create (or reuse an existing) OAuth 2.0 Desktop client in Google Cloud Console and download its client_secret.json. If the consent screen is in "Testing" publishing status, add each new user's Google account as a test user first, or auth will fail with access-denied.

  2. Place the credentials at ~/.config/ga-gsc-analytics/client_secret.json (or point GA_GSC_CLIENT_SECRET at a custom path).

  3. Grant data access: the authorizing Google account needs real permissions on the target GA4 property (Analytics Admin → Property Access Management) and the GSC site (Search Console → Settings → Users and permissions).

  4. Build the server (only needed if you change mcp/server.mjs; the repo ships a prebuilt mcp/server.bundle.cjs):

    pnpm install
pnpm run build   # bundles mcp/server.mjs -> mcp/server.bundle.cjs

  5. Register the MCP server. Either:

    • Claude Code, user-level: claude mcp add ga-gsc-analytics -- node /path/to/ga-gsc-analytics/mcp/server.bundle.cjs

    • Claude Code / Codex, project-scoped: this repo already ships a relative .mcp.json ({"mcpServers": {"ga_gsc_analytics": {"command": "node", "args": ["./mcp/server.bundle.cjs"]}}}) — copy it into a project, or symlink/vendor this whole folder in, and it's picked up automatically when that project is opened.

  6. Authorize: call the analytics_auth_start tool from within your AI tool — it opens a local browser OAuth flow and writes ~/.config/ga-gsc-analytics/tokens.json.

  7. Verify: call analytics_auth_status (should report authorized: true), then analytics_list_sources to confirm the expected GA4 properties / GSC sites show up.

Config overrides

Env var

Default

GA_GSC_CONFIG_DIR

~/.config/ga-gsc-analytics

GA_GSC_CLIENT_SECRET

$GA_GSC_CONFIG_DIR/client_secret.json

GA_GSC_TOKEN_PATH

$GA_GSC_CONFIG_DIR/tokens.json

Development

pnpm run check   # syntax check
pnpm test        # node --test tests/*.test.mjs

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

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