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Google Agent Platform Docs MCP Server

@OpenGerwin

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

OpenGerwin

Submitted by

rrromochka

About Google Agent Platform Docs MCP Server

No overview available yet

#vertex-ai-vertex-ai-api-google-agent-platform

Google Agent Platform Docs MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "google-agent-platform-docs": {
      "command": "uvx",
      "args": [
        "mcp-google-agent-platform-docs"
      ]
    }
  }
}

Google Agent Platform Docs MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

What is Google Agent Platform Docs?

An MCP server that gives AI agents direct access to Google's AI platform documentation — both the current Gemini Enterprise Agent Platform (GEAP) and the legacy Vertex AI Generative AI docs. It enables AI assistants to look up real-time documentation instead of hallucinating API details.

How to use Google Agent Platform Docs?

Install via pip install mcp-google-agent-platform-docs or uv pip install mcp-google-agent-platform-docs, then configure in any MCP client (Claude Desktop, Cursor, VS Code, Antigravity). The server provides four tools: search_docs, get_doc, list_sections, and list_models, invoked directly by the AI assistant.

Key features of Google Agent Platform Docs

  • Full-text search across 3400+ documentation pages
  • On-demand fetching with automatic caching
  • Dual source support (GEAP + Vertex AI)
  • Smart caching with 72-hour TTL and stale fallback
  • Auto-discovery of new pages via weekly sitemap scanning
  • Plug-and-play with any MCP client

Use cases of Google Agent Platform Docs

  • An AI agent looks up real-time API documentation to avoid hallucinating code examples
  • A developer browses the structure of Gemini Agent Platform docs to find relevant sections
  • An assistant retrieves the full content of a specific Vertex AI page for detailed guidance
  • An agent lists all available AI models across platforms for comparison

Frequently asked questions

1

What documentation sources are covered?

The server covers two sources: `geap` (Gemini Enterprise Agent Platform, 2300+ pages, primary) and `vertex-ai` (Vertex AI Generative AI, 1100+ pages, legacy archive).

2

How does caching work?

Pages are cached locally with a default 72-hour TTL (configurable via `MCP_DOCS_CONTENT_TTL`). On network errors, stale cached content is served as fallback. The documentation structure is cached separately with a 7-day default TTL.

3

What are the system requirements?

Python 3.10 or later and MCP 1.27.0 or later. The server uses stdio transport and works with any MCP-compatible client.

4

Can I configure the server's behavior?

Yes, via environment variables: `MCP_DOCS_CACHE_DIR` (cache location), `MCP_DOCS_CONTENT_TTL` (page cache hours), `MCP_DOCS_STRUCTURE_TTL` (structure cache days), `MCP_DOCS_DEFAULT_SOURCE` (default doc source), and `MCP_DOCS_HTTP_TIMEOUT` (HTTP timeout seconds).

5

How does the server discover documentation pages?

It scans sitemaps on a weekly basis to automatically find new pages. The discovered structure is then cached for fast browsing and search.

Comments

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