MCP 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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ingest_docsA | Re-ingest documents from the configured documents directory. Use this if search returns no results or if documents have been updated |
| searchB | Search for relevant document chunks using semantic similarity |
| get_chunkC | Retrieve a specific document chunk by its ID |
| refresh_indexB | Clear and refresh the entire document index |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| Collection Summary | Summary of the document collection including statistics and available sources |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: get_chunk retrieves a specific chunk, ingest_docs re-ingests documents, refresh_index clears and rebuilds the index, and search performs semantic similarity queries. The descriptions make it easy to differentiate between retrieval, ingestion, index management, and search operations.
All tool names follow a consistent verb_noun pattern (e.g., get_chunk, ingest_docs, refresh_index, search), using snake_case throughout. The naming is predictable and readable, with no deviations in style or convention across the set.
With 4 tools, the count is reasonable for a RAG server's core operations, covering ingestion, indexing, retrieval, and search. It feels slightly thin but well-scoped, as each tool earns its place without bloat, though additional utilities like document deletion or status checks might be considered minor gaps.
The toolset covers essential RAG workflows: ingestion (ingest_docs), index management (refresh_index), retrieval (get_chunk), and search (search). Minor gaps exist, such as no explicit update or delete operations for documents or chunks, but agents can work around this by re-ingesting or refreshing the index as needed.