{"id":"zvec-mcp-server","name":"zvec-mcp-server","af_score":65.0,"security_score":34.2,"reliability_score":31.2,"what_it_does":"Provides an MCP (Model Context Protocol) server exposing tools to manage a Zvec embedded vector database (collections, CRUD for documents, vector similarity search, index management) and optionally generate embeddings via OpenAI using environment-configured API credentials.","best_when":"You run the MCP server locally or in a controlled environment where you trust the MCP client/agent, and you want an LLM tool interface over an embedded vector DB with optional OpenAI embeddings.","avoid_when":"You need end-to-end security controls (authn/authz, tenant isolation, audit logging) documented at the MCP layer, or you cannot permit external embedding API calls.","last_evaluated":"2026-04-04T21:36:39.980897+00:00","has_mcp":true,"has_api":false,"auth_methods":["Environment-variable OpenAI API key (OPENAI_API_KEY) for embedding generation"],"has_free_tier":false,"known_gotchas":["Embedding tools require OPENAI_API_KEY and may incur external API calls; ensure agent handles missing/invalid keys gracefully.","Collection/session state: there is an “open collection into session cache” concept; agents may need to open collections before other operations.","Insert semantics: insert_documents reportedly fails if documents already exist; agents should choose insert vs upsert based on expected repeat behavior."],"error_quality":80.0}