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mcp-google-agent-platform-docs

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mcp-google-agent-platform-docs

AI 에이전트에게 Google AI 플랫폼 문서를 제공하는 MCP 서버입니다.

Python 3.10+ MCP License: MIT

OpenGerwin MCP Servers의 일부입니다.

이것은 무엇인가요?

AI 에이전트가 Google의 AI 플랫폼 문서(현재의 Gemini Enterprise Agent Platform (GEAP) 및 기존 Vertex AI Generative AI 문서)에 직접 액세스할 수 있도록 하는 MCP(Model Context Protocol) 서버입니다.

AI 어시스턴트가 API 세부 정보를 환각(hallucination)하는 대신, 실시간으로 실제 문서를 찾아볼 수 있습니다.

Related MCP server: mise-en-space

주요 기능

  • 🔍 3,400개 이상의 문서 페이지에 대한 전체 텍스트 검색

  • 📄 온디맨드 가져오기 — 필요할 때 페이지를 다운로드하고 캐시합니다.

  • 🗂️ 이중 소스 — 최신 GEAP + 기존 Vertex AI 문서

  • ⚡ 스마트 캐싱 — 72시간 TTL, 네트워크 오류 시 이전 데이터 사용(stale fallback)

  • 🗺️ 자동 검색 — 사이트맵 스캔을 통해 새로운 페이지 자동 발견(매주)

  • 🧩 플러그 앤 플레이 — Claude Desktop, Cursor, VS Code 및 모든 MCP 클라이언트와 호환

빠른 시작

설치

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

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

Claude Desktop 구성

claude_desktop_config.json에 추가하세요:

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

Antigravity (Google) 구성

~/.gemini/antigravity/mcp_config.json에 추가하세요:

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

Cursor / VS Code 구성

MCP 설정에 추가하세요:

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

도구

search_docs

키워드로 문서를 검색합니다.

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

get_doc

특정 페이지의 전체 내용을 가져옵니다.

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

list_sections

문서 구조를 탐색합니다.

list_sections(source="geap")

list_models

사용 가능한 모든 AI 모델(Gemini, Imagen, Veo, Claude 등)에 대한 빠른 참조입니다.

list_models()

문서 소스

소스 ID

플랫폼

페이지

상태

geap

Gemini Enterprise Agent Platform

2300+

기본 (현재)

vertex-ai

Vertex AI Generative AI

1100+

레거시 (아카이브)

GEAP 섹션

  • Agent Studio — 시각적 에이전트 빌더

  • Agents → Build — 런타임, ADK, Agent Garden, RAG 엔진

  • Agents → Scale — 세션, 메모리 뱅크, 코드 실행

  • Agents → Govern — 정책, 에이전트 게이트웨이, 모델 아머(Model Armor)

  • Agents → Optimize — 관측 가능성, 평가, 품질 알림

  • Models — Gemini, Imagen, Veo, Lyria, 파트너, 오픈 모델

  • Notebooks — Jupyter 튜토리얼

구성

사용자 정의를 위한 환경 변수:

변수

기본값

설명

MCP_DOCS_CACHE_DIR

~/.cache/mcp-google-agent-platform-docs

캐시 디렉토리

MCP_DOCS_CONTENT_TTL

72

페이지 캐시 TTL (시간)

MCP_DOCS_STRUCTURE_TTL

7

구조 캐시 TTL (일)

MCP_DOCS_DEFAULT_SOURCE

geap

기본 문서 소스

MCP_DOCS_HTTP_TIMEOUT

30

HTTP 타임아웃 (초)

개발

# 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

아키텍처

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/

라이선스

MIT — LICENSE를 참조하세요.

OpenGerwin MCP Servers의 일부입니다.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYes
sourceNogeap

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.7/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It mentions caching behavior and return format, but fails to disclose authentication requirements, rate limits, error handling, or what happens with invalid paths.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with a clear purpose sentence followed by Args/Returns. The examples are useful and do not feel excessive. Every sentence contributes to understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple retrieval tool with two parameters and an output schema (implied), the description covers key aspects: purpose, parameters with examples, caching behavior, and return format. It is largely complete given the tool's complexity, though missing error state details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It adds value by providing concrete path examples and explaining the source parameter's allowed values ('geap' or 'vertex-ai'). However, it doesn't clarify the exact format required for paths beyond examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get full content of a specific documentation page', using a specific verb and resource. It distinguishes from sibling tools like list_models, list_sections, and search_docs by focusing on a single page's full content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context by listing example paths for different sources and explaining the source parameter. It implicitly guides when to use this tool versus alternatives, but lacks explicit 'when not to use' instructions or direct comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.).

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must carry the burden. It describes the output as a 'quick reference' organized by family, but does not disclose potential behavioral aspects such as authentication requirements, rate limits, or whether it is read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose, and zero wasted words. Highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and an existing output schema, the description is fairly complete: it specifies what is listed and how it is organized. It could mention authentication or that it is a simple read operation, but overall adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters, so schema coverage is effectively 100%. The description does not add parameter info, but none is needed. Baseline for 0 parameters is 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all available AI models on Google's platform, organized by family. This specific verb+resource combination distinguishes it from sibling tools that deal with documents.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for listing models, but does not explicitly state when to use this tool versus alternatives. There is no exclusion or guidance on prerequisites, though siblings are clearly document-focused.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
sourceNogeap

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

B3.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description indicates it's a read operation returning structured data, but lacks details on side effects, auth requirements, or performance implications. Barely adequate given no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is concise and well-structured with Args and Returns sections, but could be slightly more efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Simple tool with one optional parameter and an output schema; description covers basic functionality but doesn't elaborate on scope or limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Description explains the 'source' parameter with allowed values, adding value beyond the schema which lacks property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it lists documentation sections with page counts, distinguishing from sibling tools like get_doc and search_docs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs alternatives; no exclusions or prerequisites mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo
sourceNogeap

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description details the return format (titles, paths, excerpts) and parameter behavior (defaults, max results, source options). It does not mention rate limits or authorization, but as a search tool with no annotations, the description provides sufficient transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with bullet points for arguments. Every sentence adds value, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, parameters, return value, and sibling tools. It could mention result ordering or pagination, but overall it is comprehensive for a search tool with an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description thoroughly explains each parameter: query with examples, max_results with default and maximum, and source with options and defaults. Since schema coverage is 0%, the description fully compensates by providing clear semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Search Google AI platform documentation' and specifies it returns matching pages with titles, paths, and excerpts. It distinguishes from sibling get_doc which is for reading full content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (searching documentation) and recommends an alternative (get_doc for reading full content). It also differentiates between documentation sources (geap vs vertex-ai).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

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

  1. 4 tool updatesv0.1.0
    • First observedget_doc
    • First observedlist_models
    • First observedlist_sections
    • First observedsearch_docs
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 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 issues

Resources

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