Claude LFR MCP

Lightning fast CPU-based semantic code search for Claude Code. Uses configurable embedding models and Qdrant vector database to provide intelligent code retrieval without requiring GPU resources.

Quick Start

Prerequisites

A running Qdrant instance is required. Start one with Docker:

docker run -p 6333:6333 qdrant/qdrant

Installation

uv tool install claude-lfr-mcp

Claude Code Setup

Add the MCP server to Claude Code project:

claude mcp add claude-lfr -- claude-lfr-mcp

Verify the installation:

/mcp

You should see claude-lfr listed with its available tools.

Then ask claude to index codebase.

Embedding Models

This tool supports multiple embedding models, allowing you to choose the best tradeoff between speed, memory usage, and retrieval quality for your use case.

Model Comparison

Model	Dimension	Size	Speed	Quality	Best For
all-MiniLM-L6-v2	384	~90MB	Fastest	Good	Quick prototyping, CI pipelines, large codebases
bge-small-en-v1.5	384	~67MB	Fast	Good+	Balance of speed and accuracy
snowflake-arctic-embed-xs	384	~90MB	Fast	Good+	Modern alternative to MiniLM
snowflake-arctic-embed-s	384	~130MB	Medium	Better	Production use with speed needs
bge-base-en-v1.5	768	~438MB	Slower	Best	Default - Best retrieval quality
nomic-embed-text-v1.5	768	~130MB	Medium	Better	When using Nomic ecosystem

Model Details

all-MiniLM-L6-v2

• ✅ Fastest inference, smallest memory footprint
• ✅ No query prefix required (simpler usage)
• ❌ Lower retrieval accuracy than larger models
• Best for: CI/CD pipelines, quick iteration, very large codebases where speed matters

bge-small-en-v1.5

• ✅ Excellent speed with better accuracy than MiniLM
• ✅ Smallest download size (~67MB)
• ❌ Requires "query: " prefix (handled automatically)
• Best for: Daily development, good balance of speed and quality

snowflake-arctic-embed-xs / snowflake-arctic-embed-s

• ✅ Modern architecture, competitive performance
• ✅ No prefix required
• ❌ Less established than BGE family
• Best for: Production deployments wanting modern alternatives

bge-base-en-v1.5 (Default)

• ✅ Best retrieval quality, well-tested
• ✅ Strong performance on code search tasks
• ❌ Larger model size (~438MB), slower inference
• Best for: When accuracy is the priority, smaller codebases

nomic-embed-text-v1.5

• ✅ Good balance of size and quality
• ✅ 768 dimensions in compact ~130MB package
• ❌ Uses different prefix convention (search_query/search_document)
• Best for: Nomic ecosystem users, medium-large codebases

Choosing a Model

Priority	Recommended Model
Speed first	all-MiniLM-L6-v2
Quality first	bge-base-en-v1.5 (default)
Balanced	bge-small-en-v1.5 or snowflake-arctic-embed-s
Modern + compact	nomic-embed-text-v1.5

Configuring the Model

Via CLI flag:

claude-lfr-index --model all-MiniLM-L6-v2 -r /path/to/code

Via environment variable:

EMBEDDING_MODEL=bge-small-en-v1.5 claude-lfr-index

List available models:

claude-lfr-index --list-models

Note: Each model uses a separate Qdrant collection (e.g., code_bge_base_en_v15, code_minilm_l6_v2). You can override this with QDRANT_COLLECTION.

MCP Tools

Tool	Description
search_code	Search indexed code by natural language query with ranked results
index_directory	Index a codebase directory for semantic search
get_index_status	Check Qdrant collection status and point count

Advanced Usage

CLI Tools

Index a codebase:

REPO_ROOT=/path/to/your/codebase claude-lfr-index

Search indexed code:

claude-lfr-search "authentication logic"
claude-lfr-search "database connection"

Environment Variables

Variable	Default	Description
QDRANT_URL	http://localhost:6333	Qdrant server URL
QDRANT_API_KEY	None	Qdrant API key (optional)
QDRANT_COLLECTION	Auto-generated	Collection name (e.g., code_bge_base_en_v15)
EMBEDDING_MODEL	bge-base-en-v1.5	Embedding model to use (see Embedding Models)
REPO_ROOT	.	Directory to index
MAX_FILES	0	Max files to index (0 = unlimited)

Manual MCP Configuration

Project-specific (.mcp.json in project root) or user-global (~/.mcp.json):

{
  "mcpServers": {
    "claude-lfr": {
      "command": "claude-lfr-mcp"
    }
  }
}

Using uvx (no install required):

{
  "mcpServers": {
    "claude-lfr": {
      "command": "uvx",
      "args": ["--from", "claude-lfr-mcp", "claude-lfr-mcp"]
    }
  }
}

Alternative Claude Code commands:

# Add to project scope
claude mcp add claude-lfr -s project -- claude-lfr-mcp

# Add globally
claude mcp add claude-lfr -s user -- claude-lfr-mcp

# Using uvx
claude mcp add claude-lfr -- uvx --from claude-lfr-mcp claude-lfr-mcp

Technical Details

How It Works

1. Indexing: Walks the codebase, chunks files into 80-line segments, generates embeddings (384 or 768 dimensions depending on model), and upserts to Qdrant in batches of 256
2. Searching: Embeds your query with the model-appropriate prefix, finds most similar code chunks via cosine similarity

Supported File Types

.py, .ts, .tsx, .js, .jsx, .rs, .go, .java, .cs, .php, .c, .cpp, .h, .hpp

Ignored Directories

.git, node_modules, .venv, dist, build, __pycache__

Development

Install from Source

git clone https://gitlab.com/derico/claude-lfr-mcp.git
cd claude-lfr-mcp
uv sync
uv tool install -e .

Running MCP Server Locally

uv run claude-lfr-mcp

Development MCP configuration:

{
  "mcpServers": {
    "claude-lfr": {
      "command": "uv",
      "args": ["run", "claude-lfr-mcp"]
    }
  }
}

Running Tests

Install dev dependencies and run tests:

uv sync --extra dev
uv run pytest

Run with coverage report:

uv run pytest --cov=claude_lfr_mcp

Run specific test file:

uv run pytest tests/test_mcp_server.py -v

Requirements

• Python 3.10+
• Running Qdrant instance