Qdrant MCP Server
by art-qalam-fr
README.md
<p align="center">
<img src="https://raw.githubusercontent.com/art-qalam-fr/Hephaistos-Kit/main/logo/hephaistos-kit_banderole.jfif" alt="Hephaistos-Kit" width="640"/>
</p>
> Ce dépôt est un **composant MCP du [Hephaistos-Kit](https://github.com/art-qalam-fr/Hephaistos-Kit)** —
> utilisable seul, mais conçu pour être cloné en sous-module et installé via `mcp/install.ps1`.
>
> 🙏 Adapté de [mhalder/qdrant-mcp-server](https://github.com/mhalder/qdrant-mcp-server) — merci aux développeurs originaux pour la base qui a permis cette intégration au kit.
---
# Qdrant MCP Server
[](https://github.com/mhalder/qdrant-mcp-server/actions/workflows/ci.yml)
[](https://codecov.io/gh/mhalder/qdrant-mcp-server)
A Model Context Protocol (MCP) server providing semantic search capabilities using Qdrant vector database with multiple embedding providers.
## Features
- **Zero Setup**: Works out of the box with Ollama - no API keys required
- **Privacy-First**: Local embeddings and vector storage - data never leaves your machine
- **Code Vectorization**: Intelligent codebase indexing with AST-aware chunking and semantic code search
- **Multiple Providers**: Ollama (default), OpenAI, Cohere, and Voyage AI
- **Hybrid Search**: Combine semantic and keyword search for better results
- **Semantic Search**: Natural language search with metadata filtering
- **Incremental Indexing**: Efficient updates - only re-index changed files
- **Configurable Prompts**: Create custom prompts for guided workflows without code changes
- **Rate Limiting**: Intelligent throttling with exponential backoff
- **Full CRUD**: Create, search, and manage collections and documents
- **Flexible Deployment**: Run locally (stdio) or as a remote HTTP server
## Quick Start
### Prerequisites
- Node.js 20+
- Docker and Docker Compose
### Installation
```bash
# Clone and install
git clone https://github.com/mhalder/qdrant-mcp-server.git
cd qdrant-mcp-server
npm install
# Start services and pull model
docker compose up -d
docker exec ollama ollama pull nomic-embed-text
# Build
npm run build
```
### Configuration
#### Local Setup (stdio transport)
Add to `~/.claude/claude_code_config.json`:
```json
{
"mcpServers": {
"qdrant": {
"command": "node",
"args": ["/path/to/qdrant-mcp-server/build/index.js"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"EMBEDDING_BASE_URL": "http://localhost:11434"
}
}
}
}
```
#### Remote Setup (HTTP transport)
> **⚠️ Security Warning**: When deploying the HTTP transport in production:
>
> - **Always** run behind a reverse proxy (nginx, Caddy) with HTTPS
> - Implement authentication/authorization at the proxy level
> - Use firewalls to restrict access to trusted networks
> - Never expose directly to the public internet without protection
> - Consider implementing rate limiting at the proxy level
> - Monitor server logs for suspicious activity
**Start the server:**
```bash
TRANSPORT_MODE=http HTTP_PORT=3000 node build/index.js
```
**Configure client:**
```json
{
"mcpServers": {
"qdrant": {
"url": "http://your-server:3000/mcp"
}
}
}
```
**Using a different provider:**
```json
"env": {
"EMBEDDING_PROVIDER": "nvidia", // or "openai", "cohere", "voyage"
"NVIDIA_API_KEY": "nvapi-...", // provider-specific API key
"QDRANT_URL": "http://localhost:6333"
}
```
Restart after making changes.
See [Advanced Configuration](#advanced-configuration) section below for all options.
## Tools
### Collection Management
| Tool | Description |
| --------------------- | -------------------------------------------------------------------- |
| `create_collection` | Create collection with specified distance metric (Cosine/Euclid/Dot) |
| `list_collections` | List all collections |
| `get_collection_info` | Get collection details and statistics |
| `delete_collection` | Delete collection and all documents |
### Document Operations
| Tool | Description |
| ------------------ | ----------------------------------------------------------------------------- |
| `add_documents` | Add documents with automatic embedding (supports string/number IDs, metadata) |
| `semantic_search` | Natural language search with optional metadata filtering |
| `hybrid_search` | Hybrid search combining semantic and keyword (BM25) search with RRF |
| `delete_documents` | Delete specific documents by ID |
### Code Vectorization
| Tool | Description |
| ------------------ | ---------------------------------------------------------------------------------------------- |
| `index_codebase` | Index a codebase for semantic code search with AST-aware chunking |
| `search_code` | Search indexed codebase using natural language queries |
| `reindex_changes` | Incrementally re-index only changed files (detects added/modified/deleted) |
| `get_index_status` | Get indexing status and statistics for a codebase |
| `clear_index` | Delete all indexed data for a codebase |
### Resources
- `qdrant://collections` - List all collections
- `qdrant://collection/{name}` - Collection details
## Configurable Prompts
Create custom prompts tailored to your specific use cases without modifying code. Prompts provide guided workflows for common tasks.
**Note**: By default, the server looks for `prompts.json` in the project root directory. If the file exists, prompts are automatically loaded. You can specify a custom path using the `PROMPTS_CONFIG_FILE` environment variable.
### Setup
1. **Create a prompts configuration file** (e.g., `prompts.json` in the project root):
See [`prompts.example.json`](prompts.example.json) for example configurations you can copy and customize.
2. **Configure the server** (optional - only needed for custom path):
If you place `prompts.json` in the project root, no additional configuration is needed. To use a custom path:
```json
{
"mcpServers": {
"qdrant": {
"command": "node",
"args": ["/path/to/qdrant-mcp-server/build/index.js"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"PROMPTS_CONFIG_FILE": "/custom/path/to/prompts.json"
}
}
}
}
```
3. **Use prompts** in your AI assistant:
**Claude Code:**
```bash
/mcp__qdrant__find_similar_docs papers "neural networks" 10
```
**VSCode:**
```bash
/mcp.qdrant.find_similar_docs papers "neural networks" 10
```
### Example Prompts
See [`prompts.example.json`](prompts.example.json) for ready-to-use prompts including:
- `find_similar_docs` - Semantic search with result explanation
- `setup_rag_collection` - Create RAG-optimized collections
- `analyze_collection` - Collection insights and recommendations
- `bulk_add_documents` - Guided bulk document insertion
- `search_with_filter` - Metadata filtering assistance
- `compare_search_methods` - Semantic vs hybrid search comparison
- `collection_maintenance` - Maintenance and cleanup workflows
- `migrate_to_hybrid` - Collection migration guide
### Template Syntax
Templates use `{{variable}}` placeholders:
- Required arguments must be provided
- Optional arguments use defaults if not specified
- Unknown variables are left as-is in the output
## Code Vectorization
Intelligently index and search your codebase using semantic code search. Perfect for AI-assisted development, code exploration, and understanding large codebases.
### Features
- **AST-Aware Chunking**: Intelligent code splitting at function/class boundaries using tree-sitter
- **Multi-Language Support**: 35+ file types including TypeScript, Python, Java, Go, Rust, C++, and more
- **Incremental Updates**: Only re-index changed files for fast updates
- **Smart Ignore Patterns**: Respects .gitignore, .dockerignore, and custom .contextignore files
- **Semantic Search**: Natural language queries to find relevant code
- **Metadata Filtering**: Filter by file type, path patterns, or language
- **Local-First**: All processing happens locally - your code never leaves your machine
### Quick Start
**1. Index your codebase:**
```bash
# Via Claude Code MCP tool
/mcp__qdrant__index_codebase /path/to/your/project
```
**2. Search your code:**
```bash
# Natural language search
/mcp__qdrant__search_code /path/to/your/project "authentication middleware"
# Filter by file type
/mcp__qdrant__search_code /path/to/your/project "database schema" --fileTypes .ts,.js
# Filter by path pattern
/mcp__qdrant__search_code /path/to/your/project "API endpoints" --pathPattern src/api/**
```
**3. Update after changes:**
```bash
# Incrementally re-index only changed files
/mcp__qdrant__reindex_changes /path/to/your/project
```
### Usage Examples
#### Index a TypeScript Project
```typescript
// The MCP tool automatically:
// 1. Scans all .ts, .tsx, .js, .jsx files
// 2. Respects .gitignore patterns (skips node_modules, dist, etc.)
// 3. Chunks code at function/class boundaries
// 4. Generates embeddings using your configured provider
// 5. Stores in Qdrant with metadata (file path, line numbers, language)
index_codebase({
path: "/workspace/my-app",
forceReindex: false // Set to true to re-index from scratch
})
// Output:
// ✓ Indexed 247 files (1,823 chunks) in 45.2s
```
#### Search for Authentication Code
```typescript
search_code({
path: "/workspace/my-app",
query: "how does user authentication work?",
limit: 5
})
// Results include file path, line numbers, and code snippets:
// [
// {
// filePath: "src/auth/middleware.ts",
// startLine: 15,
// endLine: 42,
// content: "export async function authenticateUser(req: Request) { ... }",
// score: 0.89,
// language: "typescript"
// },
// ...
// ]
```
#### Search with Filters
```typescript
// Only search TypeScript files
search_code({
path: "/workspace/my-app",
query: "error handling patterns",
fileTypes: [".ts", ".tsx"],
limit: 10
})
// Only search in specific directories
search_code({
path: "/workspace/my-app",
query: "API route handlers",
pathPattern: "src/api/**",
limit: 10
})
```
#### Incremental Re-indexing
```typescript
// After making changes to your codebase
reindex_changes({
path: "/workspace/my-app"
})
// Output:
// ✓ Updated: +3 files added, ~5 files modified, -1 files deleted
// ✓ Chunks: +47 added, -23 deleted in 8.3s
```
#### Check Indexing Status
```typescript
get_index_status({
path: "/workspace/my-app"
})
// Output:
// {
// isIndexed: true,
// collectionName: "code_a3f8d2e1",
// chunksCount: 1823,
// filesCount: 247,
// lastUpdated: "2025-01-30T10:15:00Z",
// languages: ["typescript", "javascript", "json"]
// }
```
### Supported Languages
**Programming Languages** (35+ file types):
- **Web**: TypeScript, JavaScript, Vue, Svelte
- **Backend**: Python, Java, Go, Rust, Ruby, PHP
- **Systems**: C, C++, C#
- **Mobile**: Swift, Kotlin, Dart
- **Functional**: Scala, Clojure, Haskell, OCaml
- **Scripting**: Bash, Shell, Fish
- **Data**: SQL, GraphQL, Protocol Buffers
- **Config**: JSON, YAML, TOML, XML, Markdown
See [configuration](#code-vectorization-configuration) for full list and customization options.
### Custom Ignore Patterns
Create a `.contextignore` file in your project root to specify additional patterns to ignore:
```gitignore
# .contextignore
**/test/**
**/*.test.ts
**/*.spec.ts
**/fixtures/**
**/mocks/**
**/__tests__/**
```
### Best Practices
1. **Index Once, Update Incrementally**: Use `index_codebase` for initial indexing, then `reindex_changes` for updates
2. **Use Filters**: Narrow search scope with `fileTypes` and `pathPattern` for better results
3. **Meaningful Queries**: Use natural language that describes what you're looking for (e.g., "database connection pooling" instead of "db")
4. **Check Status First**: Use `get_index_status` to verify a codebase is indexed before searching
5. **Local Embedding**: Use Ollama (default) to keep everything local and private
### Performance
Typical performance on a modern laptop (Apple M1/M2 or similar):
| Codebase Size | Files | Indexing Time | Search Latency |
|--------------|-------|---------------|----------------|
| Small (10k LOC) | 50 | ~10s | <100ms |
| Medium (100k LOC) | 500 | ~2min | <200ms |
| Large (500k LOC) | 2,500 | ~10min | <500ms |
**Note**: Indexing time varies based on embedding provider. Ollama (local) is fastest for initial indexing.
## Examples
See [examples/](examples/) directory for detailed guides:
- **[Basic Usage](examples/basic/)** - Create collections, add documents, search
- **[Knowledge Base](examples/knowledge-base/)** - Structured documentation with metadata
- **[Advanced Filtering](examples/filters/)** - Complex boolean filters
- **[Rate Limiting](examples/rate-limiting/)** - Batch processing with cloud providers
- **[Code Search](examples/code-search/)** - Index codebases and semantic code search
## Advanced Configuration
### Environment Variables
#### Core Configuration
| Variable | Description | Default |
| ----------------------------------- | -------------------------------------- | --------------------- |
| `TRANSPORT_MODE` | "stdio" or "http" | stdio |
| `HTTP_PORT` | Port for HTTP transport | 3000 |
| `EMBEDDING_PROVIDER` | "ollama", "nvidia", "openai", "cohere", "voyage" | ollama |
| `QDRANT_URL` | Qdrant server URL | http://localhost:6333 |
| `PROMPTS_CONFIG_FILE` | Path to prompts configuration JSON | prompts.json |
#### Embedding Configuration
| Variable | Description | Default |
| ----------------------------------- | -------------------------------------- | --------------------- |
| `EMBEDDING_MODEL` | Model name | Provider-specific |
| `EMBEDDING_BASE_URL` | Custom API URL | Provider-specific |
| `EMBEDDING_MAX_REQUESTS_PER_MINUTE` | Rate limit | Provider-specific |
| `EMBEDDING_RETRY_ATTEMPTS` | Retry count | 3 |
| `EMBEDDING_RETRY_DELAY` | Initial retry delay (ms) | 1000 |
| `OPENAI_API_KEY` | OpenAI API key | - |
| `COHERE_API_KEY` | Cohere API key | - |
| `VOYAGE_API_KEY` | Voyage AI API key | - |
#### Code Vectorization Configuration
| Variable | Description | Default |
| ------------------------- | ------------------------------------------------ | ------- |
| `CODE_CHUNK_SIZE` | Maximum chunk size in characters | 2500 |
| `CODE_CHUNK_OVERLAP` | Overlap between chunks in characters | 300 |
| `CODE_ENABLE_AST` | Enable AST-aware chunking (tree-sitter) | true |
| `CODE_BATCH_SIZE` | Number of chunks to embed in one batch | 100 |
| `CODE_CUSTOM_EXTENSIONS` | Additional file extensions (comma-separated) | - |
| `CODE_CUSTOM_IGNORE` | Additional ignore patterns (comma-separated) | - |
| `CODE_DEFAULT_LIMIT` | Default search result limit | 5 |
### Provider Comparison
| Provider | Models | Dimensions | Rate Limit | Notes |
| ---------- | --------------------------------------------------------------- | -------------- | ---------- | -------------------- |
| **Ollama** | `nomic-embed-text` (default), `mxbai-embed-large`, `all-minilm` | 768, 1024, 384 | None | Local, no API key |
| **OpenAI** | `text-embedding-3-small` (default), `text-embedding-3-large` | 1536, 3072 | 3500/min | Cloud API |
| **Cohere** | `embed-english-v3.0` (default), `embed-multilingual-v3.0` | 1024 | 100/min | Multilingual support |
| **Voyage** | `voyage-2` (default), `voyage-large-2`, `voyage-code-2` | 1024, 1536 | 300/min | Code-specialized |
**Note:** Ollama models require `docker exec ollama ollama pull <model-name>` before use.
## Troubleshooting
| Issue | Solution |
| ------------------------------- | ---------------------------------------------------------------------------- |
| **Qdrant not running** | `docker compose up -d` |
| **Collection missing** | Create collection first before adding documents |
| **Ollama not running** | Verify with `curl http://localhost:11434`, start with `docker compose up -d` |
| **Model missing** | `docker exec ollama ollama pull nomic-embed-text` |
| **Rate limit errors** | Adjust `EMBEDDING_MAX_REQUESTS_PER_MINUTE` to match your provider tier |
| **API key errors** | Verify correct API key in environment configuration |
| **Filter errors** | Ensure Qdrant filter format, check field names match metadata |
| **Codebase not indexed** | Run `index_codebase` before `search_code` |
| **Slow indexing** | Use Ollama (local) for faster indexing, or increase `CODE_BATCH_SIZE` |
| **Files not found** | Check `.gitignore` and `.contextignore` patterns |
| **Search returns no results** | Try broader queries, check if codebase is indexed with `get_index_status` |
| **Out of memory during index** | Reduce `CODE_CHUNK_SIZE` or `CODE_BATCH_SIZE` |
## Development
```bash
npm run dev # Development with auto-reload
npm run build # Production build
npm run type-check # TypeScript validation
npm test # Run test suite
npm run test:coverage # Coverage report
```
### Testing
**422 tests** (376 unit + 46 functional) with **98%+ coverage**:
- **Unit Tests**: QdrantManager (21), Ollama (31), OpenAI (25), Cohere (29), Voyage (31), Factory (32), MCP Server (19)
- **Functional Tests**: Live API integration, end-to-end workflows (46)
**CI/CD**: GitHub Actions runs build, type-check, and tests on Node.js 20 & 22 for every push/PR.
## Contributing
Contributions welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for:
- Development workflow
- Conventional commit format (`feat:`, `fix:`, `BREAKING CHANGE:`)
- Testing requirements (run `npm test`, `npm run type-check`, `npm run build`)
**Automated releases**: Semantic versioning via conventional commits - `feat:` → minor, `fix:` → patch, `BREAKING CHANGE:` → major.
## Acknowledgments
The code vectorization feature is inspired by and builds upon concepts from the excellent [claude-context](https://github.com/zilliztech/claude-context) project (MIT License, Copyright 2025 Zilliz).
## License
MIT - see [LICENSE](LICENSE) file.
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