{"id":"zvec-mcp-server","name":"zvec-mcp-server","homepage":"https://pypi.org/project/zvec-mcp-server/","repo_url":"https://github.com/zvec-ai/zvec-mcp-server","category":"ai-ml","subcategories":[],"tags":["mcp-server","vector-search","rag","python","pydantic","embedded-vector-database","openai-embeddings","tooling"],"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.","use_cases":["LLM agents that need to create/open Zvec collections and run vector similarity search","RAG workflows using Zvec with semantic retrieval (single or multi-vector queries)","CRUD management of embedded document stores (insert/upsert/update/delete/fetch) from an MCP-capable client","Index lifecycle management (create/drop/optimize indexes) to tune retrieval performance","Generating embeddings and writing/searching by natural-language queries through MCP tools"],"not_for":["Public multi-tenant deployments without an additional access control layer","Use as a secure server endpoint for untrusted clients (no explicit auth/authorization shown in README)","Environments where sending text to OpenAI for embeddings is not allowed","Use cases requiring strong operational guarantees like published uptime/SLA or well-documented retry/idempotency semantics"],"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.","alternatives":["Direct use of Zvec APIs/SDKs in your application","Other vector DB MCP servers (if available for your stack)","Framework-level RAG tooling (e.g., LangChain/LlamaIndex) integrated directly with Zvec instead of via MCP","Embedding/search via OpenAI + a separate vector store layer you control"],"af_score":65.0,"security_score":34.2,"reliability_score":31.2,"package_type":"mcp_server","discovery_source":["pypi"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:36:39.980897+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Environment-variable OpenAI API key (OPENAI_API_KEY) for embedding generation"],"oauth":false,"scopes":false,"notes":"No server-side authentication/authorization for MCP endpoints is described in the provided README; access appears to depend on where/how the MCP server is deployed and which client can connect."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"Cost depends on OpenAI usage for embedding tools; README does not provide pricing for the MCP server itself."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":65.0,"security_score":34.2,"reliability_score":31.2,"mcp_server_quality":78.0,"documentation_accuracy":78.0,"error_message_quality":80.0,"error_message_notes":"README claims clear, actionable errors (resource not found with suggestions, Pydantic validation errors, Zvec API context), but does not show concrete error schemas/codes or MCP-specific payload examples.","auth_complexity":65.0,"rate_limit_clarity":5.0,"tls_enforcement":40.0,"auth_strength":20.0,"scope_granularity":10.0,"dependency_hygiene":55.0,"secret_handling":55.0,"security_notes":"Likely HTTPS/TLS applies only to upstream embedding API calls (OpenAI); the MCP transport security is not described. No MCP authentication/authorization model is documented. The README suggests secrets provided via env vars (OPENAI_API_KEY), but does not state how the server logs/handles them. Dependency posture and CVE status cannot be determined from the provided content.","uptime_documented":0.0,"version_stability":45.0,"breaking_changes_history":30.0,"error_recovery":50.0,"idempotency_support":"false","idempotency_notes":"Only partial hints: tools like insert_documents are described as failing if exists, and upsert/update/delete semantics exist; no explicit idempotency guarantees or dedup/retry guidance is documented.","pagination_style":"none","retry_guidance_documented":false,"known_agent_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."]}}