Ai Readiness

AI Readiness

Overview

An "agent-ready" API is one that an AI agent can discover, understand, call correctly, and recover from errors without human intervention. Most APIs aren't there yet.

Two ways to run this check, same rubric family, different target — pick by what exists:

• collection ai-readiness <collectionId/path> scores a Postman collection — by cloud ID, local file path, or a postman/collections/<name> local-mode directory.
• spec ai-readiness <spec> scores an OpenAPI specification directly — by cloud ID or local file path — with no collection involved at all.

Scoring

The command computes and prints the score itself — read the fields it gives you, don't recompute them:

• readiness (score 0-100 + bucket) is the headline number. Buckets, low to high: Limited → Fair → Good → Excellent.
• confidence (high/medium/low) says how many signals it could actually measure vs. had to mark unknown — a data-quality caveat, not part of the score.

Interpreting Results

Report the bucket, score, and confidence the command actually printed — don't infer a percentage band. Doc coverage is a modifier via its adjustment, not a separate gate; call it out by name when it's low, since recommendations flag that first. Also state which verb ran (collection vs. spec ai-readiness), which target was scored (local path vs. cloud ID), the output mode, and — if --min-score was set — the resulting exit code, not just "it passed."

You can ask user if they would like to set this check with a min score guarantee to run on their CI.

Reference

• collection-schema-v3 skill — what saved examples and descriptions look like in the git-synced format this command reads.
• ci-integration skill — where --min-score fits as a pipeline gate alongside spec lint/collection lint/workspace lint.