vector-mcp

Select and connect the right vector-store backend for the vector-mcp MCP server — chromadb, postgres/pgvector, qdrant, couchbase, or mongodb — and supply the correct db_type/connection parameters that every collection and search call needs. Use when the agent must decide which engine to target, wire up host/port/path/credentials, or move a workload between embedded and networked backends. Do NOT use for collection lifecycle (use vector-collection-management) or for running queries (use vector-hybrid-search).

Create, populate, list, and delete vector-store collections through the vector-mcp MCP server's vector_collection_management tool. Use when the agent must stand up a new RAG collection, ingest documents (from a directory, file paths/URLs, or raw text) into an existing collection, enumerate collections, or drop one — across any supported backend (chromadb, postgres/pgvector, qdrant, couchbase, mongodb). Do NOT use for running queries against a collection (use vector-hybrid-search) or for choosing/tuning a backend connection (vector-backend-operations).

Retrieve knowledge from a vector-store collection via the vector-mcp MCP server's vector_search tool — semantic (ANN) search, lexical BM25 search, or a hybrid of the two fused with Reciprocal Rank Fusion. Use when the agent must answer a question from an indexed corpus, pull top-k relevant chunks for RAG context, or tune the semantic-vs-lexical balance of retrieval. Do NOT use for creating collections or ingesting documents (use vector-collection-management) or for backend connection setup (vector-backend-operations).