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 Related Servers Score Discussions

Python

Remote

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.

Related MCP server: mise-en-space

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.

ParametersJSON Schema

Name	Required	Description	Default
`path`	Yes
`source`	No		geap

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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

Name	Required	Description	Default
No parameters

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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

Name	Required	Description	Default
`source`	No		geap

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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

Name	Required	Default
`query`	Yes
`max_results`	No
`source`	No	geap

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

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. Dates show when Glama detected each change.

4 tool updatesv0.1.0Apr 25, 2026
- First observedget_doc
- First observedlist_models
- First observedlist_sections
- First observedsearch_docs

Install Server

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TDQS

A4.1/5.0

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

GitHub Repository

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