Footdigest: one fixture in detail, teams, status, kickoff, score, stage, venue, and the event timeline (goals, cards, subs). Ask by match_id (from get_schedule).
| Name | Required | Description | Default |
|---|---|---|---|
| match_id | Yes | Fixture id, e.g. from get_schedule. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but the description fully explains the output: teams, status, kickoff, score, stage, venue, and event timeline. No mention of side effects or auth, but for a read operation this is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first lists returned data, second gives parameter guidance. No wasted words, front-loaded with key info.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description covers the main return fields. It doesn't address edge cases (e.g., unplayed match timeline), but status is mentioned so it's adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline 3. The description adds context that match_id comes from get_schedule, which is meaningful beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves detailed info for one fixture, listing specific fields. It doesn't explicitly differentiate from sibling 'get_match_brief', but the detail level is implied.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions to query by match_id from get_schedule, giving context. It doesn't exclude alternatives or state when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of football competition analysis—brackets, match details, probabilities, standings, injuries, etc.—with no overlapping purposes that would confuse an agent.
Most tools follow a consistent 'get_' prefix with descriptive noun phrases, but 'simulate' deviates from the pattern, and 'get_head_to_head' uses hyphens. Overall, the naming is clear and predictable.
With 14 tools, the set feels well-scoped for a football competition analytics server. Each tool serves a clear purpose, covering predictions, match data, standings, and team status without unnecessary clutter.
The tool surface covers all major areas of competition analysis: schedules, standings, brackets, head-to-head, match details (including AI briefs), probabilities, model auditing, qualification scenarios, tournament odds, simulations, suspensions, and injuries. No obvious gaps for the intended domain.
