Vector Toolbox MCP
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- AlicenseNot gradedqualityBmaintenanceEnables local vector database operations with a Pinecone-compatible API, supporting exact and approximate search, metadata filtering, and semantic text queries over MCP.MIT

@vectros-ai/mcp-serverofficial
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- AlicenseAqualityDmaintenanceMCP server for OpenAI Vector Store API, managing vector stores, files, file batches, and semantic search.2112 npmMIT
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- FlicenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to perform semantic search, manage vectors, and interact with Pinecone vector databases through standardized MCP tools. Supports querying, upserting, deleting vectors and monitoring database statistics for knowledge base operations.4 npm-
TDQS
Scored across 28 tools
Several overlapping clusters exist: pinecone_search, pinecone_search_records, and pinecone_query_vectors all perform retrieval, and pinecone_describe_namespace, pinecone_describe_index_stats, pinecone_describe_index, and pinecone_index_capabilities have partially overlapping reporting purposes. The very detailed descriptions do help an agent choose correctly, but the boundaries between the search tools and the describe tools are not immediately obvious from the names alone.
Nearly all tools follow a consistent pinecone_verb_noun pattern (pinecone_create_index, pinecone_delete_records, pinecone_list_namespaces). The exceptions are pinecone_index_capabilities (noun phrase, no verb) and vectortoolbox_status (different prefix and concatenated without a separating underscore), which are minor deviations rather than a broken convention.
At 28 tools this is heavy for a single MCP server and sits above the comfortable 3-15 range. Most tools do earn their place given the breadth of the vector-DB admin surface (index, namespace, record, search, embedding, rerank), but the count is borderline and a few describe/capabilities tools could plausibly be merged.
Coverage is thorough: index lifecycle (create, create_for_model, list, describe, capabilities, configure, delete), namespace lifecycle, record CRUD (upsert documents/vectors, update, fetch, list ids, delete, purge expired), multiple search modes, embeddings, rerank, model listing, and server status. No obvious gaps for the stated vector-toolbox purpose.