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Qdrant Search MCP

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Python
Remote

Qdrant Search MCP

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

Related MCP server: semantic-code-mcp

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.

Parameter

Type

Default

Description

query

string

required

Natural language description of what to find

limit

int

10

Maximum number of results

collection

string

""

Override Qdrant collection name

list_collections

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

Payload Format Support

The server supports multiple payload formats:

Field

Kilo Code

Generic

File path

filePath

file_path, path

Code content

codeChunk

code_chunk, content, text

Start line

startLine

start_line

End line

endLine

end_line

License

MIT

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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