npm package: @iflow-mcp/qdrant-mcp-server
Description: MCP server for semantic search using local Qdrant and Ollama (default) with support for OpenAI, Cohere, and Voyage AI
Author: mhalder
Latest version: 1.5.0

# Qdrant MCP Server

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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": "openai",  // or "cohere", "voyage"
  "OPENAI_API_KEY": "sk-...",      // 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", "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.