Which integrations are available for this server?

Provides local, offline embeddings for document indexing and query embedding using Ollama models, and is the default embedding provider for the RAG memory server. Supports OpenAI-compatible /v1/embeddings endpoints for embedding document chunks and search queries, enabling use of OpenAI or compatible embedding services.

How do I use rag-mcp?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@rag-mcp search runbooks for CrashLoopBackOff fixes" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

rag-mcp

by mmelmesary

Overview Schema Source Related Servers Discussions Admin

Python

Remote

rag-mcp

A self-hosted RAG memory server for AI assistants, exposed over the Model Context Protocol (MCP). Give any MCP-capable LLM client (Claude Desktop / Claude Code, LibreChat Agents, Open WebUI via a proxy, your own tooling) searchable long-term memory over your own documents — runbooks, incident reports, RCAs, wiki exports, anything in markdown or PDF.

Read-only LLM surface — the model can only search; writes happen out-of-band through a batch ingestion job or a token-gated internal API.
Hybrid retrieval — dense semantic vectors + BM25 keyword sparse vectors, fused with Reciprocal Rank Fusion. Exact tokens (CrashLoopBackOff, error codes, resource names) are found even when embeddings miss them.
Optional cross-encoder reranking (Cohere/Jina-compatible) for precision.
Pluggable embeddings — local Ollama (offline default) or any OpenAI-compatible /v1/embeddings endpoint. One env var to switch.
Markdown + PDF ingestion with YAML front matter, heading-aware chunking, idempotent re-runs.
Vendor-neutral MCP — works with any client that speaks streamable-http MCP.

Architecture

                       WRITE PATH — populate the KB
  ┌───────────────────────────┐
  │       knowledge/**        │   your markdown & PDF docs,
  │  runbooks · incidents ·   │   optional YAML front matter
  │  wikis · post-mortems     │
  └─────────────┬─────────────┘
                │
                ▼
  ┌───────────────────────────┐   embed    ┌─────────────────────────────┐
  │        rag-ingest         │  chunks    │     embedding provider      │
  │  · parses front matter    │───────────►│   ollama (offline default)  │
  │  · heading-aware chunks   │            │   or any OpenAI-compatible  │
  │    (PDF pages = sections) │◄───────────│   /v1/embeddings endpoint   │
  │  · idempotent upserts,    │  vectors   └──────────────▲──────────────┘
  │    stale-tail cleanup     │                           │
  └─────────────┬─────────────┘                           │  the SAME
                │ upsert                                  │  provider also
                ▼                                         │  embeds queries
  ┌───────────────────────────┐                           │  (see rag-mcp ↘)
  │      Qdrant  (v1.12)      │                           │
  │    collection:  rag_kb    │                           │
  │   dense (cosine · HNSW)   │                           │
  │   bm25  (sparse · IDF)    │                           │
  └─────────────▲─────────────┘                           │
                │                                         │
                │  1. hybrid search:                      │
                │     Prefetch(dense)+Prefetch(bm25)      │
                │     fused with Reciprocal Rank Fusion   │
                │                                         │
  ┌─────────────┴───────────────────────────────┐         │
  │            rag-mcp     :8084/mcp            │         │
  │   FastMCP server — READ-ONLY tool surface   │         │
  │                                             │  2. em- │
  │   rag_search         search_incidents       │  beds   │
  │   search_runbooks     rag_collections       │◄────────┘
  │   rag_health                                │  the
  │   (+ optional step 3: cross-encoder rerank  │  query
  │     via a Cohere/Jina-compatible /rerank)   │
  └──▲──────────────────────▲───────────────────┘
     │                      │
     │  MCP                 │  POST /internal/knowledge/*
     │  streamable-http     │  capture · similar · feedback · stats
     │  (search queries)    │  token-gated; NEVER visible to the LLM
     │                      │
  ┌──┴───────────────────┐  │
  │      MCP clients     │  ▼
  │  Claude Code ·       │  trusted automation
  │  LibreChat ·         │  (agent / CI capturing
  │  your own agents     │  incidents + human feedback)
  └──────────────────────┘

Related MCP server: Markdown RAG MCP

Quickstart

Prerequisites: Docker (+ Compose v2), and for the default offline embedding path Ollama on the host:

ollama pull nomic-embed-text     # once

git clone https://github.com/mmelmesary/rag-mcp.git && cd rag-mcp
cp .env.example .env             # defaults work out of the box

docker compose up -d --build     # starts Qdrant + rag-mcp

docker compose run --rm rag-ingest   # index the sample docs in ./knowledge

Verify:

curl -s http://localhost:8084/mcp            # MCP endpoint (400 without handshake = alive)
curl -s http://localhost:6333/collections    # rag_kb exists with points

Wire it into an MCP client

The server speaks streamable-http at http://localhost:8084/mcp.

Claude Code (.mcp.json in your project):

{
  "mcpServers": {
    "rag": {
      "type": "http",
      "url": "http://localhost:8084/mcp"
    }
  }
}

LibreChat (librechat.yaml):

mcpServers:
  rag:
    type: streamable-http
    url: http://rag-mcp-server:8084/mcp   # container name when compose-networked

Any other MCP client: register an HTTP/streamable-http server at the URL above.

Tools exposed to the model

Tool	Purpose
`rag_search(query, doc_type?, cluster?, component?, limit?)`	Semantic search across the whole KB
`search_incidents(query, cluster?, component?, limit?)`	"Has this happened before?" — `doc_type=incident`
`search_runbooks(query, cluster?, component?, limit?)`	"What's the procedure?" — `doc_type=runbook`
`rag_collections()`	List collections + point counts
`rag_health()`	Reachability of Qdrant and the embedding provider

Filters: cluster is a soft narrow (empty same-cluster result retries fleet-wide); component/doc_type are hard filters.

Add your own documents

Drop markdown or PDFs under knowledge/ (subfolders become the default doc_type, e.g. knowledge/incidents/* → incident). Markdown supports optional YAML front matter:

---
title: Longhorn volume stuck attaching
type: incident
tags: [longhorn, storage]
component: longhorn
cluster: prod-eu
---
# Body...

Front matter is entirely yours: type, component, and cluster become searchable filter fields, but they are free-form labels — use them for any grouping that fits your domain (environments, customers, products, teams) or omit them for plain semantic search.

Then re-index — no rebuild needed (the job bind-mounts ./knowledge):

docker compose run --rm rag-ingest

Ingestion is idempotent (chunk IDs derive from (source, chunk index)); shrunken docs get their stale tail chunks deleted. Run it from CI/cron whenever docs change. Known limitations: deleting or renaming a file does not remove its old chunks — use docker compose run --rm rag-ingest --recreate after removals/renames.

Switching to a hosted embedding provider

# .env
EMBEDDINGS_PROVIDER=openai
EMBEDDINGS_BASE_URL=https://api.openai.com    # or LiteLLM proxy / Azure gateway / TEI
EMBEDDINGS_API_KEY=sk-...
EMBEDDINGS_MODEL=text-embedding-3-small

Then rebuild the collection (ingest and query must always share provider+model):

docker compose run --rm rag-ingest --recreate

Configuration

All configuration is environment-driven — see .env.example for the full annotated list and docs/DESIGN.md for the design rationale, retrieval-pipeline deep-dive, internal write API, and complete env table.

Internal write API (optional)

Besides the read-only MCP tools, the server exposes plain-HTTP routes (/internal/knowledge/capture|similar|feedback|stats) so a trusted automation process can write incident records ("knowledge flywheel"). These are not visible to the LLM. Gate them with RAG_INTERNAL_TOKEN. See docs/DESIGN.md.

Development

python3 -m pip install -r requirements.txt pytest
python3 -m pytest tests/
python3 server.py                       # run against a local Qdrant on :6333
QDRANT_URL=http://localhost:6333 python3 ingest.py --path knowledge

License

MIT

This server cannot be deployed

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

Maintenance

ActivityMaintained

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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rag-mcp

Architecture

Quickstart

Wire it into an MCP client

Tools exposed to the model

Add your own documents

Switching to a hosted embedding provider

Configuration

Internal write API (optional)

Development

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

RAG In A Box MCP Server

Markdown RAG MCP

OpsLens AI MCP Server

Enterprise Knowledge MCP