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chetan1521

grounded-rag-mcp

by chetan1521
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

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

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
ingest_documentsA

Ingest documents into a named collection so they can be searched.

Provide paths (files or directories of .txt/.md) and/or texts (raw strings). Documents are chunked, embedded, and indexed for both semantic and keyword search. Returns how many chunks were added and the collection's new total.

searchA

Search a collection and return the most relevant chunks, each with its source.

mode is "hybrid" (BM25 + semantic, default), "dense" (semantic only), or "bm25" (keyword only). rerank applies the cross-encoder reranker if one is configured. Each result includes per-stage scores for transparency. An empty list means nothing relevant was found — treat that as "not in the documents".

answerA

Answer a question grounded in a collection, with citations.

Retrieves the most relevant passages and asks the host's model (via MCP sampling) to answer using ONLY those passages, citing them. Returns {grounded, answer, citations}. If nothing relevant is found, grounded is false and no answer is invented. If the host does not support sampling, the grounded context is returned for the host to compose the answer itself.

list_collectionsA

List all ingested collections and how many chunks each contains.

evaluate_retrievalA

Measure retrieval quality on labeled cases: hit_rate, MRR, and recall@k.

Each case is {query, relevant_sources}. Use this to quantify quality and catch regressions — e.g. before and after changing chunking or switching embedders.

Prompts

Interactive templates invoked by user choice

NameDescription
grounded_answerA reusable prompt that instructs strict, cited, grounded answering.

Resources

Contextual data attached and managed by the client

NameDescription
collections_resourceRead-only snapshot of the ingested collections (name + chunk count).
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.2/5.0

Scored across 5 tools

Disambiguation5/5

Each tool targets a distinct operation: ingestion, search, grounded answering, collection listing, and retrieval evaluation. There is no overlap in purpose, and descriptions clearly delineate when to use each.

Naming Consistency4/5

Tool names follow a clear imperative, snake_case style. Most use verb_noun (ingest_documents, list_collections, evaluate_retrieval), though search and answer are bare verbs rather than verb_noun, creating a minor inconsistency.

Tool Count5/5

Five tools is well-scoped for a grounded RAG server: ingest, search, answer, list collections, and evaluate retrieval. Each tool covers a necessary part of the workflow without redundancy or bloat.

Completeness4/5

Core RAG workflows are covered end-to-end, including ingestion, retrieval, grounded answering, and quality evaluation. The main gap is lifecycle management: there is no way to delete or update documents or collections once ingested.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources