Vector Search MCP Server

A purpose-built MCP server enabling semantic data retrieval via vector embeddings, allowing AI systems to perform meaning-based searches in large datasets. Qdrant is a leading example, providing standard protocols for vector operations.

Source: https://qdrant.tech/

Features

• High-performance, open-source vector similarity search engine
• Handles high-dimensional vectors for large-scale AI applications
• Cloud-native scalability with vertical and horizontal scaling
• Enterprise-grade managed cloud with zero-downtime upgrades
• Quick and simple deployment via Docker
• Lean API for easy integration and local testing
• Cost-efficient storage with built-in compression and disk offloading
• Built in Rust for speed and reliability, capable of processing billions of vectors
• Integrates with leading embeddings and frameworks
• Advanced search capabilities for nuanced similarity and semantic searches
• Supports multimodal data with fast, accurate search algorithms
• Recommendation API for building responsive, personalized recommendation systems
• Supports multiple vectors in a single query for improved result relevancy
• Efficient nearest neighbor search and payload filtering for Retrieval Augmented Generation (RAG)
• Real-time data analysis and anomaly detection using vector search
• Scalable infrastructure for AI agents to handle complex, data-driven tasks

Pricing

• Get started for free (no further pricing details provided in the available content)

Tags

mcp vector-database semantic-search qdrant ai-integration