{"id":"pgvector-mcp-server","name":"pgvector-mcp-server","homepage":"https://pypi.org/project/pgvector-mcp-server/","repo_url":null,"category":"ai-ml","subcategories":[],"tags":["mcp","pgvector","postgres","vector-search","retrieval","rag","agents"],"what_it_does":"Provides an MCP (Model Context Protocol) server that exposes operations related to pgvector/pgvector-based vector search, allowing an agent to create/use embeddings-backed search capabilities against a PostgreSQL + pgvector setup via MCP tools.","use_cases":["Agent-driven semantic search over Postgres/pgvector documents","Building RAG workflows where retrieval is performed via an MCP tool","Integrating pgvector query functionality into agent toolchains"],"not_for":["Purely file-based/local vector search without Postgres/pgvector","User-facing search UI without an orchestration layer","Applications requiring strict multi-tenant isolation guarantees without additional safeguards"],"best_when":"You want agents to perform vector retrieval against a pgvector-backed PostgreSQL database using a standardized MCP tool interface.","avoid_when":"You cannot (or do not want to) expose database-backed retrieval to an agent runtime, or you need strong, documented tenant isolation and query safety controls.","alternatives":["Direct pgvector/Postgres integration in your application (no MCP)","Custom MCP server tailored to your vector retrieval and safety needs","Vendor/proprietary vector search services with first-party auth/rate limits"],"af_score":33.0,"security_score":38.8,"reliability_score":18.8,"package_type":"mcp_server","discovery_source":["pypi"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:48:36.871106+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":[],"oauth":false,"scopes":false,"notes":"No authentication/authorization details were provided in the supplied content, so auth strength and scope granularity cannot be determined from observable facts."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information was provided; as an MCP server component, costs would typically be infrastructure/database-related."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":33.0,"security_score":38.8,"reliability_score":18.8,"mcp_server_quality":45.0,"documentation_accuracy":30.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":40.0,"rate_limit_clarity":0.0,"tls_enforcement":60.0,"auth_strength":35.0,"scope_granularity":20.0,"dependency_hygiene":40.0,"secret_handling":40.0,"security_notes":"Specific security implementation details (TLS enforcement, auth mechanism, secret handling, dependency status) were not provided, so scores reflect uncertainty. As a DB-backed MCP server, treat it as having high blast radius: require least-privilege DB credentials, validate/parameterize any agent-provided SQL/query inputs, and enforce query limits to prevent data leakage and resource exhaustion.","uptime_documented":0.0,"version_stability":45.0,"breaking_changes_history":0.0,"error_recovery":30.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_gotchas":["Agent may generate broad/expensive similarity queries; ensure query limits and safety controls","Database connectivity errors must be handled; without documented retry behavior, agents may fail unrecoverably","Vector retrieval tool outputs can be large; ensure truncation/limits to avoid context bloat"]}}