Build one small HTTP boundary
Keep the API key in an environment variable read by server-side Python. Create one request function that adds the bearer header, sets connection and total timeouts, parses JSON, captures the request ID and quota headers, and maps non-success responses into explicit exceptions. Feature code should not repeat raw HTTP behavior.
Use the OpenAPI contract to select parameters and response fields. For match collections, send a bounded limit and a precise UTC or date window. Follow pagination deliberately instead of assuming the first page contains every result. Store stable IDs in your application rather than searching by name on every request.
import os
import requests
KEY = os.environ["STATS_API_KEY"]
response = requests.get(
"https://stats-api.com/api/v1/football/matches",
headers={"Authorization": f"Bearer {KEY}"},
params={"date_from": "2026-08-01", "date_to": "2026-08-07", "limit": 50},
timeout=(3.0, 10.0),
)
response.raise_for_status()
payload = response.json()Retry only failures that can improve
A 401 indicates missing or invalid credentials and should stop immediately. A 404 means the stable resource was not found; asking the same endpoint again will not fix it. A 422 requires corrected parameters. A 429 may be retried after the instructed delay if the workflow budget permits, while a 503 can receive a small bounded retry with jitter.
Return errors to the caller with enough context to act, but never include the full Authorization header. Tests should cover each branch, malformed JSON, connection failure, and an empty successful collection. This gives an AI coding assistant concrete acceptance criteria instead of the vague instruction to “handle errors.”
A 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.
Write Python that reads STATS_API_KEY from the environment.
Use an explicit connect and response timeout.
Call only paths and fields in /openapi.json.
Return typed errors for 401, 404, 422, 429, and 503, and never retry 401 or 404.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.
- Run the example with a test key in an isolated environment and inspect logs to ensure the Authorization value is redacted.
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.