Gemini RAG MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | HTTP server port | 3000 |
| DEBUG | No | Enable debug console output (true|false) | false |
| LOG_LEVEL | No | Logging level (error|warn|info|debug) | info |
| GEMINI_MODEL | No | Gemini model to use for queries (options: gemini-2.5-pro, gemini-2.5-flash) | gemini-2.5-pro |
| GOOGLE_API_KEY | Yes | Your Google API key with Gemini API access | |
| STORE_DISPLAY_NAME | Yes | Display name for your vector store/knowledge base |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| upload_fileB | Upload a file to the FileSearchStore for RAG indexing. The file will be processed and made searchable. |
| upload_contentC | Upload text content to the FileSearchStore for RAG indexing. The content will be processed and made searchable. |
| queryB | Query the FileSearchStore using RAG (Retrieval-Augmented Generation) to get answers based on uploaded documents. The AI will search through the documents and provide relevant answers with citations. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: querying for answers, uploading text content, and uploading files. There is no overlap in functionality, and an agent can easily tell them apart based on their specific roles in the RAG workflow.
All tool names follow a consistent verb_noun pattern (query, upload_content, upload_file) with clear, descriptive terms. The naming is uniform and predictable, making it easy for agents to understand and use the tools.
With 3 tools, the server is well-scoped for a RAG system, covering core operations: querying, uploading text, and uploading files. It is slightly lean but reasonable, as it handles the essential workflow without unnecessary complexity.
The tool set covers the main RAG operations: ingestion (uploading content/files) and retrieval (querying). Minor gaps might include tools for managing or deleting uploaded content, but the core functionality is complete for basic RAG use cases.