Reduce the contract to the tools the workflow needs
An OpenAPI file can describe an entire service, but an agent should not automatically receive every operation. Begin with the user story: discover a competition, resolve a team, and list fixtures inside a date window. Select only those operations and generate narrow wrappers with descriptive names, required parameters, bounded pagination, and typed return values.
This least-privilege tool surface improves reliability and security. The model has fewer similar operations to confuse, and the application can audit each external call. The wrapper should add authentication, timeouts, request IDs, and error mapping. Those responsibilities belong in code, while the model decides which valid tool to call and how to explain the result.
Use a deterministic discovery sequence
Do not begin a fixture request with a name the model turned into an ID. Search or list competitions first, retain the selected stable competition ID, then resolve the team within the intended context. Only after those choices are explicit should the agent call the match collection with a UTC date range and a bounded page size.
Return the raw IDs, match status, kickoff time, team objects, request ID, and retrieval timestamp alongside the human answer. That small evidence envelope lets a reviewer reproduce the query. It also gives downstream steps a reliable input without reparsing prose or matching a team name that may have accents, abbreviations, or several squads.
1. list_competitions(q)
2. confirm competition_id
3. list_teams(q)
4. confirm team_id
5. list_matches(team_id, date_from, date_to, limit)
6. return answer + evidenceA handoff your agent can actually follow
Treat an AI agent as a planner and transformer, not as the database. Give it a narrow task, the exact resources it may call, the response fields it may quote, and a stop condition for missing data. Keep bearer credentials in the server-side tool implementation rather than in the prompt, transcript, browser, or generated source file.
The handoff below is deliberately operational. It asks for evidence before prose, makes uncertainty visible, and keeps the model inside the current football API contract. Adapt the output format to your product, but preserve the rules about stable IDs, UTC timestamps, freshness, and error handling.
Read /openapi.json before generating code.
Create server-side tools only for competition discovery, team lookup, and filtered matches.
Read STATS_API_KEY from the environment.
Return a compact evidence object and handle 401, 404, 429, and 503 without inventing a fallback.What the human reviewer still owns
Automation can verify schemas and repeatable checks, but publication and product decisions still need a person. Review the selected competition, season, team, and match IDs; confirm that the time window matches the user’s question; and read the final answer against the retrieved JSON. A fluent explanation is not evidence that the underlying call was correct.
For time-sensitive football AI, record when the source was ingested and when the agent retrieved it. If the workflow cannot establish those timestamps, qualify the result instead of presenting it as current. The same rule applies to unavailable capabilities: do not quietly substitute fixtures or results for lineups, player statistics, odds, expected goals, injuries, or live events.
- Confirm every quoted fact appears in the retained API response.
- Exercise the empty, 401, 404, 429, and 503 paths before launch.
- Keep model interpretation separate from source facts in logs and user-facing output.
- Verify generated operation paths and parameter names against the exact OpenAPI revision used to build the tool.
Continue with the contract, not a guess
Start with the public contract and coverage ledger, then move into implementation only when the capability you need is marked available. The related guide gives your next agent-first pattern without requiring an undocumented endpoint.