Discovery is a workflow, not a guessing game
An endpoint catalog is useful when it explains where each operation came from, what the source establishes, and what remains unknown. Begin with a specification or documentation collection you are authorized to inspect. Do not treat a route appearing in a document as permission to execute it.
ApiCrawler.com focuses on the decisions around discovery: choosing a scope, preserving source details, separating examples from contracts, and preparing reviewed records for developers and AI tools. The guides cover design patterns rather than a hosted crawling service.
Three ways to organize the evidence
Specification-first
Use an approved API description to identify operations, parameters, and schemas. Preserve provider and version information so similarly named resources do not become an accidental mixture. Our AI API crawler guide develops this process with a fictional library service.
Documentation-assisted
Use reference pages and tutorials to clarify what a specification leaves unexplained. Keep their evidence separate when they disagree. A useful summary should not turn an example value into a universal limit or a required field.
Reviewed runtime observations
Only perform runtime checks within a separately approved scope. Record the environment, account boundary, and time of each observation. A successful sample request establishes less than complete compatibility. Keep partial coverage visible.
A practical first catalog
Choose one provider and one task. Create a small record with the method, path, purpose, required inputs, described outputs, security notes, and source reference. Add an uncertainty list instead of filling every empty field with a familiar convention.
Ask a second developer to trace one important claim back to its source. If that review is difficult, improve the record format before collecting more data. A smaller catalog that can be inspected is a stronger starting point than a larger catalog that obscures its assumptions.
Turn reviewed records into useful work
A catalog can support repository notes, integration planning, prompt templates, or a deliberately narrow tool interface. The output does not have to be the same for every reader. A developer may need field details; an operations teammate may need to understand when an answer is incomplete.
Follow the AI IDE knowledge-pack guide for coding context, use the prompt library for structured extraction, or review the sample catalog contract before defining your own format.
Questions to settle before the first crawl
Name the source owner and approved locations. Decide what is out of scope. Agree on how to label unverified facts, conflicting sources, and partial coverage. Keep credentials and production data out of public examples. Finally, assign someone to review meaningful source changes after the initial experiment.
These decisions are the foundation. Automation becomes more valuable after the team can explain what it is automating and where it must stop.
