Provides a local vector database system for semantic code search with zero-configuration setup and real-time file monitoring via MCP. Relevant as an MCP server solution.
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A server providing data retrieval capabilities powered by the Chroma embedding database, enabling AI models to create and retrieve data collections using vector search, full text search, and metadata filtering.
Combines Neo4j and Qdrant databases for document search with semantic relevance and context, serving as a powerful MCP server for structured and vector search.
A Pinecone MCP server providing vector search capabilities over Pinecone through the Model Context Protocol.
An MCP server providing semantic data search using embeddings and similarity matching. Facilitates AI-powered, context-aware data retrieval for development teams.
A purpose-built MCP server that enables semantic data retrieval via vector embeddings, allowing AI systems to perform meaning-based searches in large datasets. Qdrant is a leading example, offering standard protocols for vector operations.
Vectorize MCP Server enables semantic search and retrieval using natural language queries, optimized for performance on large-scale vector data sets. It supports customizable parameters like result counts and integrates Approximate Nearest Neighbors algorithms for efficient similarity matching. Highly relevant as a specialized MCP server for vector operations.
file-management-mcp-servers
Files Db MCP is a local vector database system designed for semantic code search with zero-configuration setup. It provides real-time file monitoring and integrates with coding agents via the Message Control Protocol (MCP), serving as an MCP server solution.
vector-database, semantic-search, file-monitoring, mcp
No pricing information provided; the project is open source (MIT License).