{"id":"irahardianto-qurio","name":"qurio","af_score":49.0,"security_score":22.8,"reliability_score":25.0,"what_it_does":"Qurio is a self-hosted ingestion and retrieval (RAG) engine for AI coding assistants. It crawls or ingests local documents (e.g., web pages, PDFs, Markdown), chunks content structurally, embeds it (Gemini for embeddings), stores vectors/metadata in Weaviate/PostgreSQL, and exposes retrieval to agents via an MCP server over a JSON-RPC 2.0 endpoint.","best_when":"You want localhost RAG for coding assistants and can run Docker Compose locally while allowing embedding calls to the configured provider (Gemini) and connecting your agent via MCP.","avoid_when":"You need an SDK/OpenAPI-described REST API, strong documented rate limiting, or you cannot provide/secure required API keys (Gemini, optional rerank providers).","last_evaluated":"2026-03-30T15:42:05.993313+00:00","has_mcp":true,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["MCP tools are described at a high level; exact tool argument schemas/response shapes and pagination/limit behavior are not documented in the provided README.","MCP transport is described as 'stateless, streamable HTTP'; agents may need HTTP MCP client support.","Indexing/ingestion is asynchronous; agents may query before ingestion completes unless the user coordinates workflow via the dashboard/status."],"error_quality":0.0}