Provides tools to search and fetch Google's Gemini API documentation, allowing users to query topics like function calling or embeddings and retrieve structured content from specific documentation pages.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Gemini Docs MCP Server find the documentation for function calling"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server that provides tools to search and fetch Google's Gemini API documentation.
Search Documentation: Search through the Gemini API documentation index to find relevant pages
Fetch Documentation: Fetch and parse specific documentation pages with structured content extraction
Related MCP server: Anthropic Docs MCP Server
npm install
npm run buildnode dist/index.jsAdd to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"gemini-docs": {
"command": "node",
"args": ["/path/to/gemini-docs-mcp/dist/index.js"]
}
}
}search_gemini_docsSearch the Gemini API documentation for relevant pages.
Parameters:
query (string, required): Search query (e.g., "function calling", "embeddings")
max_results (number, optional): Maximum results to return (1-20, default: 10)
response_format (string, optional): Output format - "markdown" or "json" (default: "markdown")
Example:
{
"query": "function calling",
"max_results": 5,
"response_format": "json"
}fetch_gemini_docFetch and parse a specific Gemini API documentation page.
Parameters:
path (string, required): Documentation path (e.g., "embeddings", "function-calling"). Use empty string for the main overview page.
response_format (string, optional): Output format - "markdown" or "json" (default: "markdown")
Common paths:
"" - Main overview page
quickstart - Getting started guide
function-calling - Function calling / tool use
embeddings - Text embeddings
structured-output - JSON structured output
text-generation - Text generation basics
image-understanding - Vision / image analysis
live - Live API (real-time streaming)
api-key - API key setup
models - Available models
Example:
{
"path": "function-calling",
"response_format": "markdown"
}# Install dependencies
npm install
# Development mode with auto-reload
npm run dev
# Build
npm run build
# Run tests
npm run testFetch and parse a specific Gemini API documentation page.
This tool fetches a documentation page from the Gemini API docs and extracts its content including title, sections, and code examples.
Args:
path (string): The documentation path (e.g., "function-calling", "embeddings"). Use empty string "" for the main overview page.
response_format ('markdown' | 'json'): Output format (default: 'markdown')
Returns: For JSON format: Structured data with schema: { "url": string, // Full URL of the page "title": string, // Page title "description": string, // Brief description "sections": [ { "level": number, // Heading level (1-6) "title": string, // Section title "content": string // Section content } ], "codeExamples": [ { "language": string, // Programming language "code": string // Code snippet } ], "fullText": string // Full text content (truncated if too long) }
Common paths:
"" (empty) - Main overview page
"quickstart" - Getting started guide
"function-calling" - Function calling / tool use
"embeddings" - Text embeddings
"structured-output" - JSON structured output
"text-generation" - Text generation basics
"image-understanding" - Vision / image analysis
"audio" - Audio understanding
"live" - Live API (real-time streaming)
"api-key" - API key setup
"models" - Available models
"pricing" - Pricing information
"rate-limits" - Rate limits and quotas
Use search_gemini_docs first to find the correct path if you're unsure.
Inputs: path (required), response_format.
Search the Gemini API documentation for relevant pages.
This tool searches through the Gemini API documentation index to find pages matching your query. It searches across page titles, categories, and keywords to find the most relevant documentation.
Args:
query (string): Search query to find relevant documentation (e.g., "function calling", "embeddings", "rate limits")
max_results (number): Maximum results to return, 1-20 (default: 10)
response_format ('markdown' | 'json'): Output format (default: 'markdown')
Returns: For JSON format: Structured data with schema: { "query": string, // The search query "total": number, // Number of results found "results": [ { "title": string, // Page title "path": string, // Documentation path (e.g., "function-calling") "url": string, // Full URL to the documentation page "category": string, // Category (e.g., "Core Capabilities") "matchedKeywords": [] // Keywords that matched the query } ] }
Examples:
"function calling" -> finds Function Calling documentation
"embeddings" -> finds Embeddings documentation
"authentication" or "api key" -> finds API Keys documentation
"rate limits" -> finds Rate Limits documentation
"vision" or "image" -> finds Image Understanding documentation
Use this tool first to find relevant documentation pages, then use fetch_gemini_doc to get the full content.
Inputs: query (required), max_results, response_format.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 2 tools
The two tools have clearly distinct purposes with no overlap: fetch_gemini_doc retrieves and parses specific documentation pages, while search_gemini_docs searches across the documentation index to find relevant pages. The descriptions explicitly guide users to use search first when unsure of the path, then fetch for detailed content, creating a logical workflow without confusion.
Both tools follow a perfect verb_noun pattern with consistent snake_case naming: fetch_gemini_doc and search_gemini_docs. The naming clearly indicates the action (fetch/search) and the target (gemini_doc/gemini_docs), making them predictable and easy to understand as a set.
With only 2 tools, this server feels thin for a documentation exploration domain. While the tools cover the core workflow of search-then-fetch, additional operations like listing available documentation categories or getting metadata about the documentation set would enhance completeness. The count is borderline minimal but functional.
For a documentation server, the surface covers the essential workflow: searching for relevant pages and fetching detailed content. The main gap is the lack of a tool to browse or list available documentation sections without searching, which could help agents discover content. However, the search tool with its comprehensive examples mitigates this gap reasonably well.
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