Provides tools to manage file search stores, upload documents, import files, and perform RAG queries using Google's Gemini models.
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-file-search-rag-mcp query my store for the executive summary of Q4 earnings"
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.
MCP (Model Context Protocol) server for Google's Gemini File Search (RAG). Manage file search stores, upload documents, and query with RAG through 12 tools.
Works with Claude Desktop, Cursor, VS Code, and any MCP client.
Add to claude_desktop_config.json:
{
"mcpServers": {
"gemini-rag": {
"command": "npx",
"args": ["-y", "@node2flow/gemini-file-search-rag-mcp"],
"env": {
"GEMINI_API_KEY": "your-gemini-api-key"
}
}
}
}Add to MCP settings:
{
"mcpServers": {
"gemini-rag": {
"command": "npx",
"args": ["-y", "@node2flow/gemini-file-search-rag-mcp"],
"env": {
"GEMINI_API_KEY": "your-gemini-api-key"
}
}
}
}For remote deployment or shared access:
GEMINI_API_KEY=your_key npx @node2flow/gemini-file-search-rag-mcp --httpServer starts on port 3000 (configurable via PORT env var). MCP endpoint: http://localhost:3000/mcp
Related MCP server: estudIA-MCP
Environment Variable | Required | Description |
| Yes | Google Gemini API key (get one here) |
| No | Port for HTTP server (default: |
Tool | Description |
| Create a new file search store |
| List all file search stores |
| Get store details |
| Delete a store (with optional force) |
Tool | Description |
| Upload content directly to a store (text, base64) |
| Import an existing Gemini file into a store |
Tool | Description |
| Check status of a store operation |
| Check status of an upload operation |
Tool | Description |
| List documents in a store |
| Get document details |
| Delete a document from a store |
Tool | Description |
| Query documents using RAG with Gemini models |
Node.js 18+
Google Gemini API key
Go to Google AI Studio
Click "Create API key"
Copy the key and use it as GEMINI_API_KEY
git clone https://github.com/node2flow-th/gemini-files-search-rag-mcp-community.git
cd gemini-files-search-rag-mcp-community
npm install
npm run build
# Run in stdio mode
GEMINI_API_KEY=your_key npm start
# Run in dev mode (hot reload)
GEMINI_API_KEY=your_key npm run dev
# Run in HTTP mode
GEMINI_API_KEY=your_key npm start -- --httpMIT License - see LICENSE
Copyright (c) 2026 Node2Flow
Create a new Gemini File Search store for RAG documents. Returns the created store resource. Use this to create a knowledge base before uploading documents.
Inputs: display_name (required).
Delete a document from a File Search store. Use force=true to also delete associated chunks.
Inputs: document_name (required), force.
Delete a Gemini File Search store. Use force=true to also delete all documents inside it.
Inputs: store_name (required), force.
Get details of a specific document in a File Search store, including state, size, and metadata.
Inputs: document_name (required).
Check the status of a store operation (create, delete, import). Returns whether the operation is done and any error details.
Inputs: operation_name (required).
Get details of a specific Gemini File Search store by its resource name.
Inputs: store_name (required).
Check the status of a file upload operation. Returns whether the upload is done and any error details.
Inputs: operation_name (required).
Import a file from the Gemini Files API into a File Search store. Use this for large files that were uploaded separately via the Files API. Returns an operation to track import progress.
Inputs: file_name (required), store_name (required), custom_metadata.
List documents in a Gemini File Search store. Returns document names, display names, state, size, and MIME types.
Inputs: store_name (required), page_size, page_token.
List all Gemini File Search stores. Returns store names, display names, and timestamps. Use to see available knowledge bases.
Inputs: page_size, page_token.
Query your documents using Gemini RAG. Sends a natural language query grounded in your File Search stores. Returns AI-generated answer with source citations from your documents.
Inputs: query (required), store_names (required), model, metadata_filter.
Upload content directly to a Gemini File Search store. Accepts text content or base64-encoded binary. For large files, use gemini_import_file_to_store instead. Returns an operation to track upload progress.
Inputs: content (required), mime_type (required), store_name (required), display_name, +2 more.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 12 tools
Most tools target a distinct resource+action (store CRUD, document CRUD, query), but two pairs risk confusion: gemini_get_operation vs gemini_get_upload_operation both poll operation status, and gemini_upload_to_store vs gemini_import_file_to_store both ingest content. The descriptions do clarify the distinctions (store vs upload operations; direct content vs Files API), keeping it mostly clean.
Every tool follows the same gemini_<verb>_<noun> convention (gemini_list_stores, gemini_get_store, gemini_delete_document, gemini_rag_query). The pattern is predictable and uniform across all 12 tools.
12 tools is well-scoped for a RAG/file-search server, covering store lifecycle, document lifecycle, ingestion, operation polling, and query. Each tool earns its place without redundancy.
Store lifecycle (create/get/list/delete) and document read/delete plus ingestion and query are well covered. Minor gaps exist: no store/document update or rename operation, and document creation is only via upload/import, but core workflows are complete.
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