The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the NYCfoodie listing page.
The data layer for restaurant taste: structured, editorial restaurant recommendations served to AI agents over MCP. Not another review scraper — curated guides and reviews normalised into one schema, queryable by tools.
MVP city: New York. The city is a parameter everywhere; nothing NYC-specific is hardcoded.
Hosted endpoint (Streamable HTTP): https://nycfoodie-production.up.railway.app/mcp
claude mcp add --transport http nycfoodie https://nycfoodie-production.up.railway.app/mcpsearch_restaurants · get_restaurant · compare_restaurants · find_guides · find_similar · guide_consensus · top_rated · submit_feedback
db/ — SQLite storage (better-sqlite3) with numbered SQL migrations written in Postgres-compatible DDL. The migration runner is dialect-agnostic by convention, so a Postgres adapter can replace better-sqlite3 later without touching the migrations.crawler/ — source adapters (starting with The Infatuation) that normalise into the db schema. Endpoint/field mapping: docs/infatuation-data-surface.md (step 2).mcp/ — stdio MCP server (@modelcontextprotocol/sdk) exposing search, detail, comparison and guide tools.docs/infatuation-data-surface.md.NNN_name.sql, applied once in order; see db/migrations/README.md for the Postgres-compatibility rules.*.db files or credentials.