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footdigest

by com.footdigest

simulate

Footdigest: run the seeded Monte Carlo over a competition's group stage and return each team's advancement probabilities. Pass trials and seed to control and reproduce the run; the same inputs always return the same numbers. Identify the competition by its slug (e.g. "world-cup-2026").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoSeed for reproducibility (default 42).
trialsNoSimulated tournaments (default 10000, capped at 50000).
competitionYesCompetition slug, e.g. world-cup-2026.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Description discloses deterministic behavior, defaults (42, 10000), cap (50000), and that it returns probabilities. With no annotations, this provides adequate transparency about the tool's behavior, though it could mention if it's safe/read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, no wasted words. Efficient and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers essential aspects: input parameters, determinism, output type. Lacks explicit output format or structure (no output schema), but 'advancement probabilities' is understandable for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for each parameter. Description adds defaults and cap for 'trials', and example for 'competition', enhancing understanding beyond schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'run the seeded Monte Carlo' and resource 'competition's group stage', returning advancement probabilities. Distinguishes from siblings by specifying group stage simulation, whereas siblings like get_tournament_odds and get_qualification_scenarios likely serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context: use 'competition' slug, pass 'trials' and 'seed' for control and reproducibility. Gives example slug. Lacks explicit when-not-to-use or alternatives, but is sufficient for selecting the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All footdigest toolsInspect
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TDQS

A4/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Resources