Google Agent Platform Docs

Name: Google Agent Platform Docs
Author: OpenGerwin

@OpenGerwin

代码仓库

关于 Google Agent Platform Docs

暂无概览

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

基本信息

分类

AI 与智能体

传输方式

stdio

发布者

OpenGerwin

提交者

rrromochka

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

FAQ from Google Agent Platform Docs

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).

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.

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.

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).

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.

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