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MCP RAG Server

by 0xrdan
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JavaScript
Hybrid

MCP RAG Server

An MCP (Model Context Protocol) server that exposes RAG capabilities to Claude Code and other MCP clients.

This is a standalone extraction from my production portfolio site. See it in action at

The Problem

You're using Claude Code but:

  • No access to your documents — Claude can't search your knowledge base

  • Context is manual — you're copy-pasting relevant docs into prompts

  • RAG is disconnected — your vector database isn't accessible to AI tools

  • Integration is custom — every project builds its own RAG bridge

The Solution

MCP RAG Server provides:

  • Standard MCP interface — works with Claude Code, Claude Desktop, and any MCP client

  • Full RAG pipeline — hybrid search, query expansion, semantic chunking built-in

  • Simple tools — rag_query, rag_search, index_document, get_stats

  • Zero config — point at ChromaDB and go

# In Claude Code, after configuring the server: "Search my knowledge base for articles about RAG architecture" # Claude automatically uses rag_query tool and gets relevant context

Results

From production usage:

Without MCP RAG

With MCP RAG

Manual context copy-paste

Automatic retrieval

No document search

Hybrid search built-in

Static knowledge

Live vector database

Custom integration per project

Standard MCP protocol

Design Philosophy

Why MCP?

MCP (Model Context Protocol) standardizes how AI applications connect to external tools:

┌──────────────┐ MCP Protocol ┌──────────────┐ │ MCP Client │◀────────────────────▶│ MCP Server │ │ (Claude Code)│ │ (This repo) │ └──────────────┘ └──────────────┘ │ ┌──────▼──────┐ │ RAG Pipeline │ │ (ChromaDB) │ └─────────────┘

Instead of building custom integrations, MCP provides a universal interface that any MCP-compatible client can use.

Tools Exposed

Tool

Description

rag_query

Query with hybrid search, returns formatted context

rag_search

Raw similarity search, returns chunks with scores

index_document

Add a single document

index_documents_batch

Batch index multiple documents

delete_by_source

Delete all docs from a source

get_stats

Collection statistics

clear_collection

Clear all data (requires confirmation)

Quick Start

1. Prerequisites

# Start ChromaDB docker run -p 8000:8000 chromadb/chroma # Set OpenAI API key (for embeddings) export OPENAI_API_KEY="sk-..."

2. Install & Build

git clone https://github.com/0xrdan/mcp-rag-server.git cd mcp-rag-server npm install npm run build

3. Configure Claude Code

Add to your Claude Code MCP configuration (~/.claude/mcp.json or project .mcp.json):

{ "mcpServers": { "rag": { "command": "node", "args": ["/path/to/mcp-rag-server/dist/server.js"], "env": { "OPENAI_API_KEY": "sk-...", "CHROMA_URL": "http://localhost:8000", "CHROMA_COLLECTION": "my_knowledge_base" } } } }

4. Use in Claude Code

# Restart Claude Code to load the server claude # Now Claude has access to RAG tools: "Index this document into my knowledge base: [paste content]" "Search for information about transformer architectures" "What do my docs say about error handling?"

API Reference

rag_query

Query the knowledge base with hybrid search. Returns formatted context suitable for LLM prompts.

// Input { question: string; // Required: the question to search for topK?: number; // Optional: number of results (default: 5) threshold?: number; // Optional: min similarity 0-1 (default: 0.5) filters?: object; // Optional: metadata filters } // Output { context: string; // Formatted context for LLM chunks: [{ content: string; score: number; metadata: object; }]; stats: { totalChunks: number; avgSimilarity: number; }; }

rag_search

Raw similarity search without context formatting.

// Input { query: string; // Required: search query topK?: number; // Optional: number of results (default: 10) filters?: object; // Optional: metadata filters } // Output: Array of chunks with scores

index_document

Add a document to the knowledge base.

// Input { id: string; // Required: unique identifier title: string; // Required: document title content: string; // Required: document content source: string; // Required: source identifier category?: string; // Optional: category tags?: string[]; // Optional: tags array } // Output { success: boolean; documentId: string; chunksIndexed: number; }

get_stats

Get collection statistics.

// Output { totalChunks: number; totalDocuments: number; // ... other stats from RAG pipeline }

Configuration

Environment Variables

Variable

Required

Default

Description

OPENAI_API_KEY

Yes

-

OpenAI API key for embeddings

CHROMA_URL

No

http://localhost:8000

ChromaDB URL

CHROMA_COLLECTION

No

mcp_knowledge_base

Collection name

EMBEDDING_MODEL

No

text-embedding-3-large

Embedding model

EMBEDDING_DIMENSIONS

No

Native

Reduced dimensions

Project Structure

mcp-rag-server/ ├── src/ │ ├── server.ts # Main MCP server implementation │ └── index.ts # Exports ├── mcp-config.example.json # Example Claude Code configuration ├── package.json └── README.md

Advanced Usage

Programmatic Server Creation

import { createServer } from 'mcp-rag-server'; const server = await createServer({ vectorDB: { host: 'http://custom-chroma:8000', collectionName: 'my_collection', }, rag: { topK: 10, enableHybridSearch: true, }, });

Using with Claude Desktop

Same configuration works with Claude Desktop's MCP support:

// ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) { "mcpServers": { "rag": { "command": "node", "args": ["/path/to/mcp-rag-server/dist/server.js"] } } }

Related Projects

Contributing

Contributions welcome! Please:

  1. Fork the repository

  2. Create a feature branch (git checkout -b feat/add-new-tool)

  3. Make changes with semantic commits

  4. Open a PR with clear description

License

MIT License - see LICENSE for details.

Acknowledgments

Built with Claude Code.

Co-Authored-By: Claude <noreply@anthropic.com>

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

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