Footdigest: the AI-generated brief for a match, the pre-match five-lens brief, the post-match recap, and event impacts, sourced from Footdigest's engine. Ask by match_id; optional locale (en or fr).
| Name | Required | Description | Default |
|---|---|---|---|
| locale | No | Language: en (default) or fr. | |
| match_id | Yes | Fixture id (from get_schedule). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'AI-generated' and brief types but fails to disclose data freshness, error behavior, or output structure. Lacks crucial behavioral details for a retrieval tool.
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, front-loaded with the tool's purpose, followed by usage instruction. Every word earns its place with no redundancy.
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?
With no output schema and simple parameters, the description adequately covers input but not output. Listing brief types is helpful, but agents may benefit from knowing the structure of the response. Adequate but not robust.
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% with descriptions for both parameters. The description adds no new semantic meaning beyond confirming locale options. Baseline 3 is appropriate.
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 AI-generated match briefs including pre-match, post-match, and event impacts. It distinguishes itself from sibling tools like get_match (match details) and get_head_to_head (head-to-head stats) by specifying the brief nature.
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 implies usage by instructing to ask by match_id and optional locale, but does not explicitly state when to use this tool over siblings or provide exclusions.
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.
