grounded-rag-mcp
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 | {
"listChanged": true
} |
| prompts | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ingest_documentsB | Ingest documents into a named collection so they can be searched. Provide |
| searchA | Search a collection and return the most relevant chunks, each with its source and per-stage scores. mode: hybrid (default) | dense | bm25. Empty result = not in the docs. |
| answerA | Answer a question grounded in a collection, with citations. Retrieves relevant passages and asks the host's model (via MCP sampling) to answer using only those, citing them. Refuses (grounded=false) when nothing relevant is found. |
| list_collectionsA | List all ingested collections and how many chunks each contains. |
| evaluate_retrievalA | Measure retrieval quality on labeled cases: hitRate, mrr, recallAtK. Each case is {query, relevantSources}. Use it to quantify quality and catch regressions. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| grounded_answer | Instructs strict, cited, grounded answering. |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| collections | Ingested collections and chunk counts |
TDQS
Scored across 5 tools
Each tool has a clearly distinct role: ingest_documents adds content, search retrieves chunks, list_collections inspects collections, answer produces grounded responses with citations, and evaluate_retrieval measures quality. There is no meaningful overlap that would cause an agent to select the wrong tool.
Most tool names follow a clear verb_noun pattern like ingest_documents, list_collections, and evaluate_retrieval. search and answer are single verbs but are still intuitive and consistent in style, creating only minor deviation.
Five tools is well-scoped for a grounded RAG server: ingestion, listing, retrieval, grounded answering, and evaluation. Each tool earns its place without redundancy or bloat.
The core RAG workflow is covered end-to-end, including ingestion, retrieval, grounded answering, and retrieval evaluation. The main gap is the lack of deletion or update operations for documents and collections, which agents would need for full lifecycle management.