Footdigest: the all-time head-to-head record between two men's national teams from the historical results dataset, wins, draws, goals, recent meetings, and penalty shootouts. Ask by team names.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Recent meetings to include (default 5, max 20). | |
| team1 | Yes | First team name, e.g. France. | |
| team2 | Yes | Second team name, e.g. Spain. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure burden. It only states the data source ('historical results dataset') but does not mention data freshness, caching behavior, or any side effects. It does not clarify that the tool is read-only or if authentication is required.
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?
The description is a single concise sentence that front-loads the purpose. The brand prefix 'Footdigest:' is minor noise but does not detract significantly. Every word is relevant, making it efficient.
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?
The tool has no output schema and is relatively simple. The description covers the main outputs (wins, draws, goals, recent meetings, penalty shootouts) but does not specify the exact return format or field definitions. This is adequate but leaves some ambiguity.
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 reinforces that team1 and team2 are team names ('Ask by team names') but adds no new meaning beyond the schema descriptions. The limit parameter is not mentioned, so no extra value.
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 provides 'all-time head-to-head record' between two men's national teams, specifying included details like wins, draws, goals, recent meetings, and penalty shootouts. It distinguishes itself from sibling tools like get_match or get_standings by focusing on historical head-to-head data.
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 'Ask by team names,' implying the need to provide two team names. However, it lacks explicit guidance on when to use this tool versus alternatives, such as get_match for individual matches, or any prerequisites like exact name formats.
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.
