Which integrations are available for this server?

Provides search and retrieval of Google AI platform documentation, including Gemini Enterprise Agent Platform (GEAP) and legacy Vertex AI documentation.

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mcp-google-agent-platform-docs

by OpenGerwin

Overview Schema Source Related Servers Discussions Admin

Python

Remote

mcp-google-agent-platform-docs

MCP server providing Google AI platform documentation to AI agents.

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.

Related MCP server: gemini-search-mcp

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 ID	Platform	Pages	Status
`geap`	Gemini Enterprise Agent Platform	2300+	Primary (current)
`vertex-ai`	Vertex AI Generative AI	1100+	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:

Variable	Default	Description
`MCP_DOCS_CACHE_DIR`	`~/.cache/mcp-google-agent-platform-docs`	Cache directory
`MCP_DOCS_CONTENT_TTL`	`72`	Page cache TTL (hours)
`MCP_DOCS_STRUCTURE_TTL`	`7`	Structure cache TTL (days)
`MCP_DOCS_DEFAULT_SOURCE`	`geap`	Default documentation source
`MCP_DOCS_HTTP_TIMEOUT`	`30`	HTTP 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

Available Tools

4 tools

get_docA

Get full content of a specific documentation page.

Args: path: Documentation page path, e.g.: GEAP paths: - "models/gemini/3-1-pro" - "build/runtime/quickstart" - "scale/memory-bank/setup" - "govern/policies/overview" - "optimize/evaluation/agent-evaluation" - "agent-studio/overview" Vertex AI paths: - "multimodal/function-calling" - "rag-engine/rag-overview" - "models/gemini/2-5-flash" source: "geap" (default) or "vertex-ai"

Returns: Complete page content in Markdown format. If not cached, fetches live from the documentation site.

Inputs: path (required), source. Returns: result

list_modelsA

List all available AI models on Google's platform.

Returns a quick reference of all models organized by family: Google (Gemini, Imagen, Veo, Lyria), Partners (Claude, Grok, Mistral, Llama), and Open Models (DeepSeek, Qwen, Kimi, etc.).

Inputs: no named parameters. Returns: result

list_sectionsB

List all documentation sections and their page counts.

Args: source: "geap" (default) or "vertex-ai"

Returns: Structured overview of all available documentation sections with descriptions and page counts.

Inputs: source. Returns: result

search_docsA

Search Google AI platform documentation.

Args: query: Search terms (e.g. "function calling", "Memory Bank setup", "Agent Development Kit", "Gemini 3.1 Pro") max_results: Number of results to return (default: 5, max: 20) source: Documentation source: - "geap" (default) — Gemini Enterprise Agent Platform (current) - "vertex-ai" — Vertex AI Generative AI (legacy)

Returns: Matching documentation pages with titles, paths, and excerpts. Use get_doc(path) to read the full content of any result.

Inputs: query (required), max_results, source. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

Deploy Server

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

TDQS

A4.1/5.0

Scored 2026-04-25 09:35 across 4 tools

Disambiguation5/5

Each tool has a distinct purpose: get_doc retrieves page content, list_models lists models, list_sections lists documentation sections, and search_docs searches across docs. No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_doc, list_models, list_sections, search_docs), making them predictable and easy to understand.

Tool Count5/5

With 4 tools, the server is well-scoped for a documentation access interface, covering essential operations without bloat.

Completeness5/5

The tool set provides comprehensive coverage for documentation: listing models, browsing sections, searching, and retrieving full content. No obvious gaps.

Maintenance

ActivityInactive

ResponsivenessNo recent community issues

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mcp-google-agent-platform-docs

What is this?

Features

Quick Start

Install

Configure Claude Desktop

Configure Antigravity (Google)

Configure Cursor / VS Code

Tools

search_docs

get_doc

list_sections

list_models

Documentation Sources

GEAP Sections

Configuration

Development

Architecture

License

Available Tools

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Google Docs API MCP Server

gemini-search-mcp

notebooklm-mcp

OMOS-MCP

`search_docs`

`get_doc`

`list_sections`

`list_models`