Most site search APIs were designed for JavaScript frontends, not AI systems. They return large payloads, inconsistent fields, and presentation details that matter to a visual UI but add noise to retrieval workflows.
Agents need compact responses that are easy to parse and rank. A strong search endpoint exposes only the fields that support downstream reasoning: headline, summary, slug, category, and a canonical URL. That gives the agent enough structure to decide whether to fetch the full article without overwhelming it with UI concerns.
Precision matters as much as shape. Query handling should be explicit, missing parameters should fail clearly, and result ordering should reflect editorial relevance rather than accidental database defaults. Rate limiting also matters because public machine-facing APIs attract bursty traffic patterns very quickly.
The best search API is not the one with the most fields. It is the one that helps another system make a clean next decision. In practice, that means predictable JSON, stable semantics, and a tight link back to canonical content.