Footdigest: a competition's fixtures with teams, kickoff time, status, score, and stage. Give the competition slug; optionally filter by status (e.g. scheduled, live, finished) and cap with limit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max fixtures to return (default 20, max 100). | |
| status | No | Optional status filter, e.g. finished. | |
| competition | Yes | Competition slug, e.g. world-cup-2026. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral aspects. It discloses the output fields and input constraints, but lacks details on error handling, rate limits, or data freshness. For a simple list tool this is adequate but not thorough.
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 clear sentences: first describes output, second explains inputs. No fluff, every word adds value, and the most important information is front-loaded.
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?
For a tool with 3 parameters and 100% schema coverage, the description covers the essential inputs and outputs. It could mention default ordering or pagination, but overall it is sufficiently complete for the tool's simplicity.
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 is 3. The description adds value by explaining the competition parameter as a 'slug', giving examples for status filter (scheduled, live, finished), and confirming that limit caps results. This enhances comprehension beyond the 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 the tool retrieves fixtures for a competition, listing included fields (teams, kickoff time, status, score, stage). It distinguishes itself from sibling tools like get_match (single match) or get_standings by focusing on the schedule of multiple matches.
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 explicitly tells when to use the tool: provide a competition slug and optionally filter by status and limit. It does not explicitly state when not to use or name alternatives, but the context of sibling tools implies the distinction.
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.
