Qdrant Search

An MCP (Model Context Protocol) server for semantic code search via Qdrant vector database. Designed to work with codebases indexed by Kilo Code or similar tools.

Features

• Semantic code search - find code by meaning, not just exact strings
• Multiple embedding providers - OpenRouter, OpenAI, or local (Ollama)
• Kilo Code compatible - works with payload formats from Kilo Code's indexer
• Collection listing - browse available Qdrant collections with stats

Quick Start

Prerequisites

• Python 3.10+
• A Qdrant instance (cloud or local)
• An embedding API (OpenRouter, OpenAI, or local Ollama)

Installation

# Clone the repo
git clone https://github.com/sandeep-wt/qdrant-search-mcp.git
cd qdrant-search-mcp

# Create a virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Configuration

Set these environment variables:

# Required
export QDRANT_URL="https://your-qdrant-instance.example.com"
export QDRANT_API_KEY="your-qdrant-api-key"
export COLLECTION_NAME="your-collection-name"

# Embedding provider (default: openrouter)
export EMBEDDING_PROVIDER="openrouter"  # or "openai" or "local"

# For OpenRouter
export OPENROUTER_API_KEY="your-openrouter-key"
export EMBEDDING_MODEL="qwen/qwen3-embedding-8b"

# For OpenAI
# export OPENAI_API_KEY="your-openai-key"
# export EMBEDDING_MODEL="text-embedding-3-small"

# For local (Ollama)
# export EMBEDDING_URL="http://localhost:11434/api/embeddings"
# export EMBEDDING_MODEL="nomic-embed-text"

Running

python -m qdrant_search_mcp

MCP Client Configuration

Claude Desktop / Cursor / Kilo Code

Add to your MCP settings:

{
  "mcpServers": {
    "qdrant-search": {
      "command": "python",
      "args": ["-m", "qdrant_search_mcp"],
      "cwd": "/path/to/qdrant-search-mcp",
      "env": {
        "QDRANT_URL": "https://your-qdrant-url",
        "QDRANT_API_KEY": "your-key",
        "COLLECTION_NAME": "your-collection",
        "EMBEDDING_PROVIDER": "openrouter",
        "OPENROUTER_API_KEY": "your-openrouter-key",
        "EMBEDDING_MODEL": "qwen/qwen3-embedding-8b"
      }
    }
  }
}

Hermes Agent

hermes mcp add qdrant-search \
  --command python \
  --args "-m,qdrant_search_mcp" \
  --cwd /path/to/qdrant-search-mcp \
  --env QDRANT_URL=https://... \
  --env QDRANT_API_KEY=... \
  --env COLLECTION_NAME=... \
  --env OPENROUTER_API_KEY=... \
  --env EMBEDDING_MODEL=qwen/qwen3-embedding-8b

Available Tools

semantic_code_search

Search the codebase index using semantic (meaning-based) search.

list_collections

List available Qdrant collections with stats (point count, vector dimensions).

Payload Format Support

The server supports multiple payload formats:

License

MIT