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Code Vector Sync

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Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible c

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About

Enables semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings, allowing natural language queries from MCP-compatible clients like Claude Desktop.

README

An MCP (Model Context Protocol) server that provides semantic code search over a local codebase using Qdrant vector embeddings and OpenAI embeddings. Point it at a directory and query it with natural language from any MCP-compatible client (e.g., Claude Desktop).

What It Does

  • Watches a local directory for file changes and auto-indexes them
  • Embeds code using OpenAI's embedding API
  • Stores vectors in a Qdrant cloud collection
  • Serves semantic search results via the MCP protocol

Architecture

File Watcher → Embedding Service → Qdrant Manager
                                        ↑
MCP Client (Claude) ←── MCP Server ────┘
                         (code_search)

Setup

1. Install dependencies

pip install -r requirements.txt

2. Configure environment

cp .env.example .env
# Edit .env with your Qdrant URL, Qdrant API key, OpenAI API key, and watch directory

3. Run the MCP server

python run_mcp_server.py

4. Connect to Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "code-vector-search": {
      "command": "python",
      "args": ["/path/to/code-vector-sync/run_mcp_server.py"],
      "env": {
        "QDRANT_URL": "your-qdrant-url",
        "QDRANT_API_KEY": "your-api-key",
        "OPENAI_API_KEY": "your-openai-key"
      }
    }
  }
}

Project Structure

code-vector-sync/
├── src/
│   ├── mcp_server.py        # MCP server entry point and tool definitions
│   ├── code_search.py       # Search query handling
│   ├── code_sync_service.py # Orchestrates watching, embedding, and indexing
│   ├── embedding_service.py # OpenAI embedding calls
│   ├── file_watcher.py      # Watchdog-based directory monitoring
│   └── qdrant_manager.py    # Qdrant client and collection management
├── run_mcp_server.py        # Launcher script
├── requirements.txt
├── .env.example             # Template — copy to .env and fill in values
└── .gitignore

Requirements

  • Python 3.10+
  • Qdrant Cloud account (free tier works)
  • OpenAI API key

Related Projects

  • agent-dev — Containerized AI agent dev environment that this server is designed to complement

from github.com/Abernaughty/code-vector-sync

Installing Code Vector Sync

This server has no published package — it is built from source. Open the repository and follow its README.

▸ github.com/Abernaughty/code-vector-sync

FAQ

Is Code Vector Sync MCP free?

Yes, Code Vector Sync MCP is free — one-click install via Unyly at no cost.

Does Code Vector Sync need an API key?

No, Code Vector Sync runs without API keys or environment variables.

Is Code Vector Sync hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Code Vector Sync in Claude Desktop, Claude Code or Cursor?

Open Code Vector Sync on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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