AI interfaces

AI interfaces measures whether a project exposes machine-readable interfaces: formal API schemas, a Model Context Protocol server, or runnable examples. Where they exist, an AI agent can consume the software through a contract instead of reverse-engineering behavior from source.

• Category: AI Readiness (15% within category)
• Weight in overall index: 0% — part of the independent AI Readiness badge
• Metric key: ai_interfaces
• null when the repository exposes none of the signals — absence is treated as not-applicable, never as a penalty

How the value is computed

The null rule, explained

A plain utility library legitimately has no API schema, no MCP surface, and possibly no examples directory — and it would be wrong to mark it down for its nature. The metric therefore only produces a value when at least one signal is present; otherwise it is null and the AI Readiness category renormalizes onto its other metrics, following the methodology-wide missing-data rule (see the health index).

Why these three signals

• API schemas are contracts a model can read exactly — endpoints, types, and errors without inference.
• An MCP server is the strongest possible statement of agent readiness: the project ships a first-class interface for AI tooling.
• Runnable examples are executable documentation. For an agent, a working example is a verified starting point rather than prose to interpret — and examples double as a de-facto test of the public API.

Improving the value

• Publish the API schema the project already implies: generate OpenAPI from the framework, commit the .proto or GraphQL SDL files.
• Maintain an examples/ directory with small, runnable programs kept current in CI.
• Where the project naturally serves tooling, consider shipping an MCP server.

Related: AI agent context · AI Readiness