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grounded-rag-mcp

grounded-rag-mcp

by chetan1521

grounded-rag-mcp

An MCP server that gives any LLM host grounded, cited retrieval over your own documents — hybrid retrieval (BM25 + dense), cross-encoder reranking, citations, and a built-in eval harness.

Point it at a folder of documents. Your MCP host (Claude Desktop, an IDE, a custom agent) can then search and answer over them — grounded in the real text, with citations, and an honest "not in the documents" path.

CI


Why

Most RAG-over-MCP examples are toys. This one is built production-flavored:

  • Hybrid retrieval — BM25 (exact terms) + dense (semantics), fused with Reciprocal Rank Fusion.

  • Cross-encoder reranking — precision on the top candidates without blowing latency.

  • Grounding + citations — answers cite their sources; if the answer isn't in the docs, it says so.

  • Built-in eval — measure retrieval quality (recall@k, MRR, hit-rate), not just vibes.

  • Local-first — the default path runs with no external services or API keys.

  • Both transports — stdio and Streamable HTTP.

Related MCP server: insight-mcp

Status

🚧 Early development. Building in public, phase by phase (see PROJECT_REQUIREMENTS.md).

  • Phase 0 — scaffold, packaging, CI

  • Phase 1 — core retrieval (chunk → embed → BM25 + dense → RRF)

  • Phase 2 — MCP server (stdio) with ingest / search

  • Phase 3 — rerank + grounding + answer (via MCP sampling)

  • Phase 4 — tests, types, docs, resource + prompt

  • Phase 5 — Streamable HTTP transport + evaluate_retrieval

  • Phase 6 — publish to PyPI

Install

pip install grounded-rag-mcp            # lean, local-first default (no torch)
pip install "grounded-rag-mcp[st]"      # + sentence-transformers for semantic embeddings & reranking

Tools

Tool

What it does

ingest_documents

Chunk, embed, and index files or raw text into a named collection

search

Hybrid / dense / bm25 retrieval, optional rerank, per-stage scores

answer

Grounded, cited answer via MCP sampling; refuses when nothing is found

list_collections

List collections and chunk counts

evaluate_retrieval

hit_rate / MRR / recall@k on labeled cases

Also exposes a resource (rag://collections) and a prompt (grounded_answer).

Use it with an MCP host (e.g. Claude Desktop)

Add to your host's MCP config:

{
  "mcpServers": {
    "grounded-rag": {
      "command": "grounded-rag-mcp"
    }
  }
}

Or run it directly:

grounded-rag-mcp            # stdio (default, for local hosts)
grounded-rag-mcp --http     # Streamable HTTP on 127.0.0.1:8000 (remote / multi-client)

Use the retrieval engine as a Python library

from grounded_rag_mcp.collection import Collection
from grounded_rag_mcp.embeddings import HashingEmbedder
from grounded_rag_mcp.ingest import load_texts
from grounded_rag_mcp.config import RetrievalConfig

col = Collection("kb", HashingEmbedder(dim=512))
col.add(load_texts(["The refund policy allows returns within 30 days of purchase."]))

for hit in col.retrieve("refund policy", RetrievalConfig(top_k=1)):
    print(hit.chunk.source, hit.score, hit.stage_scores)

Development

pip install -e ".[dev]"
ruff check . && ruff format --check . && mypy src && pytest -q

See docs/ARCHITECTURE.md, docs/BUILD_STORY.md, and PUBLISHING.md.

License

MIT © Chetan C

Available Tools

5 tools
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.

Inputs: query (required), top_k, collection. Returns: object

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.

Inputs: cases (required), mode, top_k, collection. Returns: object

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.

Inputs: paths, texts, chunk_size, collection, +1 more. Returns: object

list_collectionsA

List all ingested collections and how many chunks each contains.

Inputs: no named parameters. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    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

    ActivityNo data
    ResponsivenessOutdated data

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