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Three tools with basic verb-noun naming (get_odds, get_event_odds, get_events). All have descriptions (10 - 200 chars, within baseline). Input schemas present with type definitions (array, string). However, parameter descriptions are minimal or missing context. Output schemas are undocumented, LLMs cannot infer what fields to expect from responses. No error handling guidance, no pagination support despite potential for large result sets, no field-level documentation for chaining. Pydantic models exist (OddsQuery, EventOddsQuery, EventQuery) but are not exposed in tool definitions, limiting schema visibility.
Get odds for a specific event (match) for selected betting markets.
Get in-play and forthcoming events (matches) for the specified team.
Get odds for all forthcoming events (matches) for selected betting markets.
Output schemas undocumented. Tools return Dict or List but LLMs cannot infer field names, types, or structure. Breaks downstream tool chaining and forces agents to guess at response shape.
Parameter 'markets' lacks enum constraint. Free-form string array invites hallucinated values. Should declare valid markets (h2h, spreads, totals, outrights) as enum.
No pagination support. Tools may return hundreds of events/odds but accept no limit or offset parameters. Large responses risk context window exhaustion.
Parameter 'team' in get_events is optional but description is vague ('Optional. If present...'). No guidance on format (team name, ID, abbreviation) or behavior when omitted.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 52 | <=2025-11-25 | v2 |
No error recovery guidance. make_request() returns None on failure with only logger.error() output. LLMs receive no actionable error message, cannot distinguish retryable from fatal failures.