Skip to main content

Qdrant MCP Server for OpenCode - semantic search for code and documentation

Project description

qmcp - QDrant MCP Server for OpenCode

Version Python License MCP

Semantic search server for code and documentation using Qdrant vector database.

Language: English | Русский

Features

  • Semantic Search: Find code and documentation using natural language queries
  • Multi-language Support: Python, Go, JavaScript, TypeScript, Java, C#, Markdown
  • Live Updates: File watcher for automatic reindexing
  • Incremental Indexing: Only index changed files
  • Gitignore Support: Respects .gitignore - excludes node_modules, __pycache__, .venv, build artifacts, etc.
  • Cleanup: Remove stale vectors for deleted/changed files
  • Diagnostics: Introspection tools to understand what's indexed
  • OpenCode Skill: Natural language interface for Qdrant management

Installation

Via PyPI (Recommended)

pip install qmcp-qdrant

Via uv

uv tool install qmcp-qdrant

From Source

git clone https://github.com/BigKAA/qmcp.git
cd qmcp
make install

Quick Start

1. Ensure Qdrant is running

For Kubernetes deployment, see Qdrant on Kubernetes.

2. Add MCP Server to OpenCode

opencode mcp add qmcp-qdrant qmcp-qdrant

⚠️ Note: Environment variables must be set in ~/.config/opencode/opencode.json config file (see below).

💡 Global MCP, multiple projects: qmcp now uses a two-level auto-indexing strategy. The server automatically starts a watcher for configured WATCH_PATHS, and OpenCode/agents should ensure the current workspace is watched with qdrant_watch_ensure(paths=[workspace_root]).

3. That's It!

OpenCode will automatically discover and use the semantic search tools.

Manual Configuration (Alternative)

If opencode mcp add doesn't work, edit ~/.config/opencode/opencode.json directly:

{
  "mcp": {
    "qmcp-qdrant": {
      "type": "local",
      "command": ["qmcp-qdrant"],
      "environment": {
        "QDRANT_URL": "http://192.168.218.190:6333",
        "WATCH_PATHS": "/home/user/shared-docs,/home/user/shared-snippets"
      }
    }
  }
}

For Python module:

{
  "mcp": {
    "qmcp-qdrant": {
      "type": "local",
      "command": ["python", "-m", "qmcp.server"],
      "environment": {
        "QDRANT_URL": "http://192.168.218.190:6333",
        "WATCH_PATHS": "/home/user/shared-docs,/home/user/shared-snippets"
      }
    }
  }
}

Indexing Notes

⚠️ Important: Full indexing and reindexing of large projects can take a significant amount of time (minutes to hours depending on project size).

For large codebases, prefer:

  • Incremental reindex (mode="incremental") - only updates changed files based on content hashes
  • File watcher - enables automatic live updates when files change

Automatic Indexing Strategy

qmcp supports automatic indexing on two levels:

  1. Server startup level — on MCP startup, the server automatically tries to start a watcher for WATCH_PATHS.
  2. Workspace session level — because one global MCP server can be shared across multiple repositories, agents should check the watcher state for the current workspace and call qdrant_watch_ensure(paths=[workspace_root]) when the workspace path is missing.

Recommended OpenCode flow for every new workspace:

status = qdrant_get_status()

# If watcher is not active or the current repo is not covered,
# safely extend the watcher without dropping other projects.
qdrant_watch_ensure(paths=["/absolute/path/to/current/workspace"])

qdrant_watch_ensure merges the current workspace with already watched paths and WATCH_PATHS, making it safe for a single global MCP shared by multiple projects.

The indexer automatically respects .gitignore files, significantly reducing index size by excluding:

  • Dependencies: node_modules/, vendor/, .venv/, .pip cache/
  • Build artifacts: dist/, build/, *.class, *.o, *.so
  • Generated files: __pycache__/, *.pyc, *.pyo, .pytest_cache/
  • IDE settings: .idea/, .vscode/, *.swp, *.swo
  • Environment files: .env, .env.local, *.log

Configuration

Environment Variable Default Description
QDRANT_URL http://localhost:6333 Qdrant server URL
QDRANT_API_KEY (none) Qdrant API key (optional)
EMBEDDING_MODEL BAAI/bge-small-en-v1.5 Embedding model
EMBEDDING_CACHE_DIR (system temp) Custom directory for model cache
WATCH_PATHS /data/repo Baseline paths to watch automatically on server startup
BATCH_SIZE 50 Batch size for indexing
DEBOUNCE_SECONDS 5 Debounce delay
LOG_LEVEL INFO Logging level
LOG_FORMAT text Log format (text or json)

💡 Model Cache: Set EMBEDDING_CACHE_DIR to persist models across restarts. First launch downloads the model (~13MB), subsequent launches use cached version.

MCP Tools

Search & Indexing

Tool Description
qdrant_search Semantic search in code/docs
qdrant_index_directory Index a directory
qdrant_reindex Reindex (full or incremental)

💡 Tip: Use qdrant_search with filters for precise results:

  • chunk_type — filter by code type (function_def, class_def, etc.)
  • symbol_name — find exact symbol by name
  • language — filter by programming language

See docs/STRUCTURED_METADATA.md for detailed examples.

Collection Management

Tool Description
qdrant_list_collections List all collections
qdrant_get_collection_info Get collection info
qdrant_delete_collection Delete collection

Diagnostics & Introspection (NEW)

Tool Description
qdrant_diagnose_collection Full collection diagnostics - vectors, files, types, issues
qdrant_list_indexed_files Paginated list of indexed files with metadata
qdrant_diff_collection Compare Qdrant state with filesystem (orphans, missing, modified)

Maintenance

Tool Description
qdrant_cleanup Clean stale vectors (dry-run supported)
qdrant_watch_start Start file watcher
qdrant_watch_ensure Ensure workspace path is watched without dropping other projects
qdrant_watch_stop Stop file watcher
qdrant_get_status Server status

Diagnostic Tools Usage Examples

# Diagnose a collection - see what's indexed, file types, issues
qdrant_diagnose_collection(collection="myproject")

# List indexed files with pagination
qdrant_list_indexed_files(collection="myproject", limit=50, offset=0)

# Filter by file type
qdrant_list_indexed_files(collection="myproject", file_type=".py")

# Compare Qdrant state with filesystem
qdrant_diff_collection(collection="myproject", repo_path="/path/to/repo")

OpenCode Skill

The project includes an OpenCode skill for natural language management of Qdrant.

Installation

# Copy skill to OpenCode skills directory
cp -r skills/qmcp-manager ~/.config/opencode/skills/

Usage

Once installed, OpenCode will automatically activate the skill when you ask questions like:

Query What Happens
what's indexed in my Qdrant? Diagnoses collection and shows stats
show collection stats Lists all collections with vector counts
clean up orphans in my index Finds and previews deletion of orphaned vectors
diagnose my index Full diagnostics with issues and file list
compare index with /path/to/repo Shows orphans, missing, and modified files
find missing files Lists files on disk but not indexed
is my index up to date? Compares hashes to detect changes

Workflows Provided by Skill

  1. Quick Status - Check collection state with qdrant_list_collections
  2. Full Diagnostics - Detailed analysis with qdrant_diagnose_collection
  3. Diff - Compare Qdrant with filesystem using qdrant_diff_collection
  4. Safe Cleanup - Preview with dry-run, then confirm deletion
  5. Smart Reindex - Incremental updates based on file hashes

Skill Location

skills/qmcp-manager/SKILL.md

OpenCode Integration

Add MCP Server

opencode mcp add qmcp-qdrant qmcp-qdrant

Or edit ~/.config/opencode/opencode.json directly (required for environment variables):

{
  "mcp": {
    "qmcp-qdrant": {
      "type": "local",
      "command": ["qmcp-qdrant"],
      "environment": {
        "QDRANT_URL": "http://192.168.218.190:6333",
        "WATCH_PATHS": "/home/user/shared-docs,/home/user/shared-snippets"
      }
    }
  }
}

Recommended OpenCode Agent Workflow

For a single global MCP server shared across multiple projects:

  1. Call qdrant_get_status() when the agent starts working in a workspace.
  2. If watcher_active is false or watched_paths does not include the current workspace root, call:
qdrant_watch_ensure(paths=["/absolute/path/to/current/workspace"])

This preserves configured startup paths and already watched projects instead of replacing them.

Manage MCP Servers

opencode mcp list          # List all MCP servers
opencode mcp debug qmcp-qdrant     # Debug connection issues
opencode mcp logout qmcp-qdrant    # Remove MCP server

Development

make install      # Install dependencies
make test         # Run tests
make lint         # Lint code
make format       # Format code
make mcp-dev      # Run with MCP inspector

Troubleshooting

If you encounter issues, see the Troubleshooting Guide for:

License

Apache 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

qmcp_qdrant-0.2.2.tar.gz (199.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

qmcp_qdrant-0.2.2-py3-none-any.whl (45.6 kB view details)

Uploaded Python 3

File details

Details for the file qmcp_qdrant-0.2.2.tar.gz.

File metadata

  • Download URL: qmcp_qdrant-0.2.2.tar.gz
  • Upload date:
  • Size: 199.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for qmcp_qdrant-0.2.2.tar.gz
Algorithm Hash digest
SHA256 985b4f585969eb05f78ba6224b61f03b4cc743161591663920d3b3c6861ef416
MD5 a44d90ed4e69475b14e68819e32d3cf6
BLAKE2b-256 c65918abfc7efa8cf0281e8aa0a05a310a1d1225a1bf98230cb8c2efd553704e

See more details on using hashes here.

File details

Details for the file qmcp_qdrant-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: qmcp_qdrant-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 45.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.3

File hashes

Hashes for qmcp_qdrant-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 2cb77878ec117ecdd2f0f1bc1fd624777ed2c9b7ff125ec3d169c4d43d680a48
MD5 0d9a9e121cc5e6215aa83e3f869f67e6
BLAKE2b-256 44ec98cf82a59dab8e598bf4a2e7671acb8277f7036b8c259880a86906f5e7ad

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page