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Google Agent Platform Docs

Google Agent Platform Docs

Local
github.com/OpenGerwinAI & Agents1Updated 26w ago

About Google Agent Platform Docs

Google Agent Platform Docs is an MCP server for AI agents and assistants that need current documentation for Gemini Enterprise Agent Platform and legacy Vertex AI Generative AI. It supports full-text search and on-demand page retrieval.

Key features

  1. 01Search 3,400+ documentation pages
  2. 02Fetch and cache pages on demand
  3. 03Browse GEAP and Vertex AI docs
  4. 04List available models

Use cases

  1. 01Look up platform API details
  2. 02Retrieve documentation for agent workflows

MCP support

Exposes documentation search, page retrieval, section browsing, and model listing as tools for MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"google-agent-platform-docs": {
"command": "uvx",
"args": [
"mcp-google-agent-platform-docs"
]
}
}
}
Run commanduvx mcp-google-agent-platform-docs

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypimcp-google-agent-platform-docs

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

mcp-google-agent-platform-docs

MCP server providing Google AI platform documentation to AI agents.

Python 3.10+ MCP License: MIT

Part of OpenGerwin MCP Servers

What is this?

An MCP (Model Context Protocol) 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.

Instead of hallucinating API details, your AI assistant can look up the actual documentation in real-time.

Features

  • 🔍 Full-text search across 3400+ documentation pages
  • 📄 On-demand fetching — pages are downloaded and cached as you need them
  • 🗂️ Dual source — current GEAP + legacy Vertex AI documentation
  • ⚡ Smart caching — 72-hour TTL, stale fallback on network errors
  • 🗺️ Auto-discovery — new pages found via sitemap scanning (weekly)
  • 🧩 Plug & play — works with Claude Desktop, Cursor, VS Code, any MCP client

Quick Start

Install
# Using pip
pip install mcp-google-agent-platform-docs

# Using uv (recommended)
uv pip install mcp-google-agent-platform-docs
Configure Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "google-agent-platform-docs": {
      "command": "mcp-google-agent-platform-docs"
    }
  }
}
Configure Antigravity (Google)

Add to ~/.gemini/antigravity/mcp_config.json:

{
  "mcpServers": {
    "google-agent-platform-docs": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/mcp-google-agent-platform-docs",
        "run",
        "mcp-google-agent-platform-docs"
      ]
    }
  }
}
Configure Cursor / VS Code

Add to your MCP settings:

{
  "mcpServers": {
    "google-agent-platform-docs": {
      "command": "mcp-google-agent-platform-docs",
      "transport": "stdio"
    }
  }
}

Tools

search_docs

Search documentation by keywords.

search_docs("Memory Bank setup", source="geap")
search_docs("function calling", source="vertex-ai")
get_doc

Get full content of a specific page.

get_doc("scale/memory-bank/setup", source="geap")
get_doc("multimodal/function-calling", source="vertex-ai")
list_sections

Browse documentation structure.

list_sections(source="geap")
list_models

Quick reference for all available AI models (Gemini, Imagen, Veo, Claude, etc.).

list_models()

Documentation Sources

Source IDPlatformPagesStatus
geapGemini Enterprise Agent Platform2300+Primary (current)
vertex-aiVertex AI Generative AI1100+Legacy (archive)
GEAP Sections
  • Agent Studio — Visual agent builder
  • Agents → Build — Runtime, ADK, Agent Garden, RAG Engine
  • Agents → Scale — Sessions, Memory Bank, Code Execution
  • Agents → Govern — Policies, Agent Gateway, Model Armor
  • Agents → Optimize — Observability, Evaluation, Quality Alerts
  • Models — Gemini, Imagen, Veo, Lyria, Partners, Open Models
  • Notebooks — Jupyter tutorials

Configuration

Environment variables for customization:

VariableDefaultDescription
MCP_DOCS_CACHE_DIR~/.cache/mcp-google-agent-platform-docsCache directory
MCP_DOCS_CONTENT_TTL72Page cache TTL (hours)
MCP_DOCS_STRUCTURE_TTL7Structure cache TTL (days)
MCP_DOCS_DEFAULT_SOURCEgeapDefault documentation source
MCP_DOCS_HTTP_TIMEOUT30HTTP timeout (seconds)

Development

# Clone
git clone https://github.com/OpenGerwin/mcp-google-agent-platform-docs.git
cd mcp-google-agent-platform-docs

# Install dependencies
uv sync

# Run server locally
uv run mcp-google-agent-platform-docs

# Test with MCP Inspector
uv run mcp dev src/mcp_google_agent_platform_docs/server.py

Architecture

mcp-google-agent-platform-docs/
├── sources/                    # YAML source configurations
│   ├── geap.yaml               # GEAP (primary)
│   └── vertex-ai.yaml          # Vertex AI (legacy)
├── src/mcp_google_agent_platform_docs/
│   ├── server.py               # FastMCP server + 4 tools
│   ├── source.py               # Source model (YAML loader)
│   ├── fetcher.py              # HTML → Markdown converter
│   ├── cache.py                # TTL cache manager
│   ├── discovery.py            # Sitemap-based page discovery
│   ├── search.py               # TF-IDF search engine
│   └── config.py               # Global configuration
└── tests/

License

MIT — see LICENSE.


Part of OpenGerwin MCP Servers

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.