MCP Docs RAG Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 |
|---|---|
| list_documentsA | List all available documents in the DOCS_PATH directory. Always use this tool first to check if desired documents already exist before adding new ones. |
| rag_queryA | Query a document using RAG. Note: If the index does not exist, it will be created when you query, which may take some time. |
| add_git_repositoryB | Add a git repository to the docs directory with optional sparse checkout. Please do not use 'docs' in the document name. |
| add_text_fileC | Add a text file to the docs directory with a specified name. Please do not use 'docs' in the document name. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| guide_documents_usage | Guide on how to use documents and RAG functionality |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: add_git_repository and add_text_file handle different source types for adding documents, list_documents provides discovery, and rag_query enables retrieval. There is no overlap in functionality, making tool selection unambiguous.
All tools follow a consistent verb_noun pattern with snake_case: add_git_repository, add_text_file, list_documents, rag_query. The naming is predictable and readable throughout the set.
Four tools is reasonable for a RAG server focused on document management and querying, though it feels slightly minimal. The count supports core workflows without being overwhelming, but could potentially benefit from additional utilities like document deletion or index management.
The toolset covers adding documents (via git or text), listing, and querying, but lacks update or delete operations for document management. This creates a gap where agents cannot modify or remove documents, which may lead to dead ends in workflows requiring document lifecycle management.