ErrLookup

ErrLookup is a structured database of error messages from popular open source libraries, built so that coding agents and AI assistants can look up exactly what went wrong and how to fix it. It documents over two thousand errors across thirteen repositories, covering everything from the exact message text to the underlying cause, ordered solutions, and defensive patterns you can apply to prevent the error in the first place. The data is pinned to specific commit SHAs for accuracy, so answers stay grounded rather than drifting as libraries evolve.

The problem it solves is one developers and AI tools hit constantly. You get an error from SQLAlchemy, curl, or Prisma, paste it into your agent, and the model hallucinates a plausible but wrong fix because it has no structured reference for what that message actually means in context. ErrLookup closes that gap by providing a machine consumable API and an MCP server that agents can query directly. Instead of guessing, the agent fetches the canonical cause and a ranked list of solutions, then proceeds with confidence.

Coverage currently spans thirteen codebases across multiple languages: Docker CLI and Hugo in Go, SQLAlchemy, Flask, and Requests in Python, Prisma in TypeScript, Axios and Express in JavaScript, curl and Redis in C, and Tokio and clap in Rust. Each entry includes the exact error message regex, the file and line where it originates, a human readable cause explanation, and ordered fix steps the agent can follow. The dataset ships with a search endpoint for partial matching, so you can feed in a fragment of the error and get back relevant hits.

It is aimed at developers who use coding agents or build products that consume library errors, answer engine maintainers who want grounded responses, and teams writing internal tooling that needs robust error handling context. If you are building an agent that interacts with any of the covered libraries, wiring in ErrLookup gives it a structured reference layer it would otherwise lack.

What makes it different from generic documentation search is the machine first design. The data is structured JSON, the MCP server can be installed via npm and queried offline after the initial cache download, and the schema is stable so agents can parse responses programmatically. It also requires no API key, so integration is frictionless. You install the package, point your agent at it, and start querying.

Access is free. The MCP server is available as an npm package and caches data locally for offline use. Standard Beagle, the AI UX agency behind the project, maintains the dataset and releases periodic updates as libraries evolve. If you rely on any of the covered repositories and want your agents to stop guessing at error causes, this is worth plugging in.