Acme
This is a demo directory website built with Ever Works
A Pinecone MCP server providing vector search capabilities over Pinecone through the Model Context Protocol.
MCP server for Milvus/Zilliz vector databases, enabling direct interaction with your database through the MCP protocol.
VikingDB MCP server with collection/index introduction, vector store, and vector search capabilities.
An MCP server providing semantic data search using embeddings and similarity matching. Facilitates AI-powered, context-aware data retrieval for development teams.
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 Qdrant MCP server that enables MCP protocol support for Qdrant vector databases.
An official MCP Server for Qdrant, providing GDPR-compliant vector search and integration of AI memory with chat platforms via the Model Context Protocol.
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.
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.
An MCP server integrating Pinecone for advanced vector management, offering features like automatic namespace partitioning, metadata-aware chunking, and cost-optimized upserts for high-performance data recall.
The official MCP Server for Pinecone, enabling multi-tenant vector search and privacy-safe personalization within the MCP ecosystem.
Page 1 of 2