MCP Qdrant Semantic Search
Provides integration with OpenAI's embedding models to convert text into vector embeddings for semantic search in the Qdrant vector database.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Qdrant Semantic SearchSearch for our API authentication methods"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
๐ง MCP Qdrant Semantic Search
A Model Context Protocol (MCP) server that gives Claude persistent semantic memory via Qdrant, a high-performance vector database.
๐ฏ What is this?
This MCP server allows Claude to:
๐พ Store information with semantic search capabilities
๐ Retrieve content based on meaning, not just keywords
๐ง Remember conversations, code, documentation
๐ฏ Intelligently search through a knowledge base
Real-World Use Cases
Semantic Code Search: "Find me code that handles JWT authentication"
Team Knowledge Base: Store and retrieve procedures, best practices
Conversational Memory: Claude remembers preferences and context
Smart Documentation: Retrieve docs even with different phrasing
โจ Features
7 Available MCP Tools
Tool | Description |
| Store information with semantic indexing |
| Search by semantic similarity |
| Delete a memory by ID |
| Retrieve a specific memory |
| List all memories with pagination |
| Get collection statistics |
| Delete all memories |
๐๏ธ Architecture
โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ
โ Claude โ โโMCPโโโบโ MCP Server โ โโโโโโโบ โ Qdrant โ
โ Desktop โ โ (TypeScript)โ โ Vector DB โ
โโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโโ โโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโ
โ OpenAI โ
โ Embeddings โ
โโโโโโโโโโโโโโโโ๐ Quick Start
Prerequisites
Node.js 18+
Docker
OpenAI API Key
Claude Desktop
Installation
# 1. Install dependencies
npm install
# 2. Configure environment
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY
# 3. Start Qdrant
docker-compose up -d
# 4. Build the project
npm run build
# 5. Configure Claude Desktop
# See INSTALL.md for detailsFor complete installation, see INSTALL.md.
๐ Usage
Examples in Claude Desktop
1. Store Information
Store this information: "Our API uses JWT for authentication.
Tokens expire after 24h and must be renewed via /refresh-token"Response:
{
"success": true,
"message": "Memory stored successfully",
"id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
"content": "Our API uses JWT for authentication..."
}2. Semantic Search
Search for how to handle user sessionsClaude will use search_memory and find the JWT information even if the exact words don't match!
3. Store Code with Metadata
Store this code with tags "authentication" and "nodejs":
function validateToken(token) {
try {
return jwt.verify(token, process.env.JWT_SECRET);
} catch (error) {
throw new Error('Invalid token');
}
}4. Advanced Search with Filters
Search for authentication code, only JavaScript snippetsClaude can use filters to refine the search.
5. Get Statistics
Show me my semantic memory statsResponse:
{
"success": true,
"stats": {
"name": "semantic_memory",
"points_count": 42,
"status": "green"
},
"embedding_model": "text-embedding-3-large",
"embedding_dimensions": 1536
}๐ Key Concepts
Embeddings (Vectors)
Embeddings transform text into numerical vectors that capture semantic meaning.
# Conceptual
"JWT authentication" โ [0.234, -0.567, 0.891, ..., 0.123]
"Token security" โ [0.219, -0.543, 0.876, ..., 0.134]
# These two vectors are close = similar meaning!Cosine Similarity
Qdrant uses cosine similarity to measure "semantic proximity" between two vectors.
Score 1.0: Identical
Score 0.8-0.9: Very similar
Score 0.7: Similar (default threshold)
Score < 0.7: Less similar
Collections
A collection is like a database table, but optimized for vectors.
๐ง Configuration
Environment Variables
Variable | Description | Default |
| OpenAI API Key (required) | - |
| Qdrant server URL |
|
| Qdrant Cloud API Key (optional) | - |
| Collection name |
|
| OpenAI model |
|
| Vector dimensions |
|
Available Embedding Models
Model | Dimensions | Cost | Accuracy |
| 1536 | $ | โญโญโญ |
| 3072 | $$$ | โญโญโญโญโญ |
๐ MCP API
store_memory
{
content: string, // Content to store
metadata?: { // Optional metadata
tags?: string[],
category?: string,
source?: string,
// ... other fields
}
}search_memory
{
query: string, // Natural language query
limit?: number, // Number of results (default: 5)
threshold?: number, // Min score 0-1 (default: 0.7)
filter?: object // Metadata filters
}delete_memory
{
id: string // Memory ID
}get_memory
{
id: string // Memory ID
}list_memories
{
limit?: number, // Number of results (default: 10)
offset?: string // Starting ID for pagination
}get_stats
No parameters. Returns collection statistics.
clear_all_memories
{
confirm: boolean // Must be true to confirm
}๐งช Advanced Examples
1. Team Knowledge Base
Store these:
1. "Staging server accessible via staging.example.com,
port 3000, credentials in 1Password"
2. "To deploy to production, use 'npm run deploy:prod'
after tests pass and PR approval"
3. "Rate limiting is 1000 req/min per API key,
10000/min for enterprise clients"Then search:
How do I deploy to production?
What are the API limits?2. Semantic Code Search
Store this code:
// Metadata: language=javascript, topic=authentication
async function authenticateUser(email, password) {
const user = await db.users.findByEmail(email);
if (!user) throw new Error('User not found');
const valid = await bcrypt.compare(password, user.passwordHash);
if (!valid) throw new Error('Invalid credentials');
return generateJWT(user);
}Search:
How to verify user credentials?
Show me login code3. Conversational Memory
Store my preferences:
- I prefer TypeScript over JavaScript
- I use React 18 with hooks
- My code style follows Airbnb ESLint
- I want JSDoc comments on public functionsClaude will remember this in future conversations!
๐ Advanced Features
Hybrid Search (Vector + Filters)
// In Claude
Search for authentication code,
only Python snippets created after 2024-01-01The server can combine semantic search with metadata filters.
Chunking for Large Documents
For storing large documents, split into chunks:
const chunkSize = 500; // words
const chunks = splitIntoChunks(document, chunkSize);
for (const chunk of chunks) {
await storeMemory({
content: chunk,
metadata: {
document_id: "doc-123",
chunk_index: i,
total_chunks: chunks.length
}
});
}๐ Troubleshooting
Error: "OPENAI_API_KEY is required"
Check that the API key is defined in the Claude Desktop config file.
Qdrant Connection Error
# Check if Qdrant is running
docker ps | grep qdrant
# Restart if needed
docker-compose restartEmpty Search Results
Lower the
threshold(e.g., 0.5 instead of 0.7)Check if there's data:
get_statsRephrase the query
High OpenAI Costs
Use
text-embedding-3-small(5x cheaper)Reduce dimensions to 512 or 1024
Cache frequent embeddings
๐ Future Improvements
Support for Ollama (free local embeddings)
Web interface for visualizing memories
Collection export/import
Multi-modal support (images + text)
History-based recommendations
Automatic memory clustering
Analytics and search insights
๐ Resources
INSTALL.md - Detailed installation guide
๐ค Contributing
Contributions are welcome! Feel free to:
Open issues for bugs or suggestions
Submit Pull Requests
Improve documentation
๐ License
MIT License - See LICENSE for details.
๐ Acknowledgments
Note: This project is for educational and demonstration purposes. For production use, consider security, scalability, and costs.
Made with โค๏ธ to learn MCP and semantic search
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