{"id":"ayunis-core-ayunis-legal-mcp","name":"ayunis-legal-mcp","af_score":49.0,"security_score":36.5,"reliability_score":20.0,"what_it_does":"Provides an MCP server (FastMCP) and supporting FastAPI “Store API” for importing, storing (PostgreSQL + pgvector), and semantic-searching German legal texts (scraped/parsed from gesetze-im-internet.de). Includes a CLI for importing/querying and an MCP tool set for AI assistants to search and retrieve legal sections.","best_when":"You control the deployment (Docker), embedding endpoint (Ollama), and are building an assistant workflow that benefits from MCP tools plus a vector-backed search backend.","avoid_when":"You need turnkey managed service, standardized SLAs, or you cannot provide/secure an Ollama endpoint and credentials; also avoid if you require documented pagination/idempotency semantics for safe retries.","last_evaluated":"2026-03-30T13:50:41.503427+00:00","has_mcp":true,"has_api":true,"auth_methods":["No auth described for Store API or MCP server in provided README","OLLAMA_AUTH_TOKEN for calling Ollama embeddings (outbound to embedding provider)"],"has_free_tier":false,"known_gotchas":["Semantic search depends on embeddings; failures can occur if Ollama model output dimensionality does not match the fixed 2560-vector schema.","Rate limits and retry backoff guidance are not documented; agents may retry aggressively and cause load.","Auth/authorization for the exposed APIs is not described; running publicly could be risky."],"error_quality":0.0}