qdrant-mcp

qdrant-mcp

MCP server that gives AI coding agents persistent, semantic memory via Qdrant vector search.

Built for Claude Code but works with any MCP-compatible client.

"How does the price sync work?"

  ┌─ codebase ──────── sync-prices.ts (function syncFromAPI)
  ├─ documentation ─── API reference: POST /products/sync
  ├─ business_rules ── "Manual discounts override synced prices"
  └─ decisions ─────── ADR-007: Pull model chosen over webhooks

Key Features

Workspace-aware search — detects uncommitted changes via git status. Modified files return fresh content from disk, not stale embeddings. New files are discovered even before their first commit.

4 knowledge collections — code, documentation, business rules, architectural decisions. Search one or all at once.

Content-hashed indexing — only re-embeds chunks that actually changed. Full reindex of a large project takes seconds, not minutes.

Incremental by default — git post-commit hook triggers indexing of changed files in the background. Zero manual effort after setup.

Architecture

┌────────────────────────────────────────────────────────┐
│                  Claude Code Agent                      │
│                        │                               │
│                   MCP Protocol                         │
└────────────────────────┬───────────────────────────────┘
                         │
                         ▼
┌────────────────────────────────────────────────────────┐
│               qdrant-mcp (this server)                  │
│                                                        │
│  Tools:                                                │
│    search_codebase   search_docs   search_business     │
│    search_decisions  search_all    reindex              │
│                                                        │
│  Workspace-Aware Layer:                                │
│  ┌──────────────┐ ┌───────────────┐ ┌───────────────┐  │
│  │WorkspaceState│→│FreshContent   │→│NewFiles       │  │
│  │ git status   │ │Resolver       │ │Matcher        │  │
│  │ dirty/new/del│ │ disk override │ │ path+keyword  │  │
│  └──────────────┘ └───────────────┘ └───────────────┘  │
└────────────────────────┬───────────────────────────────┘
                         │
                         ▼
┌────────────────────────────────────────────────────────┐
│                   Qdrant (Docker)                       │
│                                                        │
│  ┌──────────┐ ┌──────────────┐ ┌────────────────────┐  │
│  │ codebase │ │documentation │ │ business_rules     │  │
│  └──────────┘ └──────────────┘ └────────────────────┘  │
│  ┌──────────────────┐                                  │
│  │    decisions      │                                  │
│  └──────────────────┘                                  │
└────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

• Node.js >= 20
• Docker (for Qdrant)
• OpenAI API key (or Ollama for local embeddings)

Install

git clone https://github.com/dutchakdev/qdrant-mcp.git
cd qdrant-mcp
npm install
npm run build

Configure

cp .env.example .env

Edit .env:

QDRANT_URL=http://localhost:6333
EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=sk-your-key-here
PROJECT_ROOT=/path/to/your/project

Setup (one command)

npm run setup

This will:

1. Start Qdrant via Docker Compose
2. Create all 4 collections with proper indexes
3. Run initial codebase indexing
4. Install git post-commit hook

Connect to Claude Code

Add to your .claude/settings.json or claude_desktop_config.json:

{
  "mcpServers": {
    "project-knowledge": {
      "command": "node",
      "args": ["/absolute/path/to/qdrant-mcp/dist/index.js"],
      "env": {
        "PROJECT_ROOT": "/path/to/your/project",
        "QDRANT_URL": "http://localhost:6333",
        "EMBEDDING_PROVIDER": "openai",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

MCP Tools

| Tool | Description | |------|-------------| | search_codebase | Semantic code search. Filters by language, project. Workspace-aware. | | search_docs | Documentation search across all indexed sources. | | search_business | Business rules and domain logic. Filter by domain. | | search_decisions | Architectural decisions, ADRs, resolved issues. | | search_all | Hybrid search across all collections at once. | | reindex | Trigger re-indexing (full or changed mode). |

How Workspace Awareness Works

The core problem: you edit a file, but Qdrant still has the old embedding. The agent gets stale context.

The solution is a 3-component pipeline that runs on every search:

WorkspaceState — calls git status --porcelain=v2 (cached 2s). Knows which files are modified, new, deleted, or renamed.

FreshContentResolver — if a Qdrant result points to a dirty file, reads the fresh version from disk and replaces the stale content. Tries to extract the same symbol (function/class) via regex for precision.

NewFilesMatcher — discovers relevant new files that Qdrant doesn't know about. Three-phase matching: path tokens → content keywords → (optional) on-the-fly embedding.

Overhead: ~10-30ms per search. Negligible compared to embedding + vector search latency.

See RACE-CONDITIONS.md for the full edge case matrix.

CLI

# One-time setup (Qdrant + collections + initial index + git hook)
npm run setup

# Index changed files (default: since last commit)
npm run index

# Full reindex
npm run index:full

# Watch mode (real-time indexing on file save)
npm run watch

# Show collection stats
npm run status

Embedding Providers

OpenAI (default)

EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=sk-...
OPENAI_EMBEDDING_MODEL=text-embedding-3-small  # 1536 dim, ~$0.02/1M tokens

Ollama (local, free)

EMBEDDING_PROVIDER=ollama
OLLAMA_URL=http://localhost:11434
OLLAMA_EMBEDDING_MODEL=nomic-embed-text  # 768 dim

Install the model first: ollama pull nomic-embed-text

Project Structure

src/
├── index.ts                 # MCP server entry point
├── cli.ts                   # CLI (setup, index, watch, status)
├── config.ts                # Environment-based configuration
├── types/
│   └── index.ts             # Shared type definitions
├── embeddings/
│   └── provider.ts          # OpenAI + Ollama embedding providers
├── qdrant/
│   ├── client.ts            # Qdrant client singleton + init
│   └── collections.ts       # Collection schemas + setup
├── indexers/
│   └── code-indexer.ts      # Code chunking + content-hashed indexing
├── search/
│   └── workspace-aware-search.ts  # Main search pipeline
├── workspace/
│   ├── workspace-state.ts         # Git status → dirty/new/deleted detection
│   ├── fresh-content-resolver.ts  # Stale → fresh content replacement
│   ├── new-files-matcher.ts       # New file discovery (path + keyword + embedding)
│   └── index.ts
└── tools/
    └── definitions.ts       # MCP tool schemas

Customization

Adding new collections

Edit src/qdrant/collections.ts to add a collection, then add a corresponding search tool in src/tools/definitions.ts and handler in src/index.ts.

Indexing non-code content

The documentation and business_rules collections are ready but need custom indexers for your data sources. Create an indexer in src/indexers/ following the CodeIndexer pattern.

Adjusting chunking

The code indexer uses regex-based chunking by default (functions, classes, exports). For more precise AST-based chunking, replace the regex patterns in code-indexer.ts with tree-sitter parsing.

License

MIT