zvec-mcp-server
OfficialRelated Servers
Alternatives to zvec-mcp-server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables AI agents to store, search, and manage a local vector database for RAG knowledge retrieval and long-term memory, powered by zvec.1Apache 2.0
- FlicenseNot gradedqualityDmaintenanceEnables LLMs to interact with Milvus vector database for search, query, and collection management operations.8-
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to interact with a Qdrant vector database, including embedding generation and vector search operations.MIT
- AlicenseNot gradedqualityDmaintenanceProvides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.MIT
- FlicenseNot gradedqualityDmaintenanceEnables large language models to interact with Milvus vector databases through natural language, supporting semantic search with built-in OpenAI-compatible embedding services and comprehensive collection management.-
- AlicenseNot gradedqualityCmaintenanceEnables semantic search and document management using a local Qdrant vector database with OpenAI embeddings. Supports natural language queries, metadata filtering, and collection management for AI-powered document retrieval.7837MIT
TDQS
Scored across 17 tools
Each tool targets a distinct operation: collection lifecycle (create/open/destroy/info/optimize), document CRUD (insert/update/upsert/delete/fetch), index management (create/drop), and search variants (vector_query, embedding_search, multi_vector_query). There is no apparent overlap or ambiguity.
All tools follow a consistent snake_case verb_noun pattern (e.g., create_and_open_collection, fetch_documents, generate_dense_embedding). There is no mixing of conventions, and the naming clearly conveys the action and resource.
17 tools cover the full vector database workflow without being excessive. Each tool serves a clear purpose, from collection management to high-level embedding search, making the surface appropriately scoped.
The tool set covers most essential operations: collection CRUD, document CRUD, indexing, multiple search modes, and embedding generation. A minor gap is the lack of a tool to list all available collections, which may require external file system access, but the core workflow is complete.