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football_simulate_group

Read-onlyIdempotent

Simulate a World Cup qualifying group via Monte Carlo to estimate each team's finishing-position, automatic/best-third advancement probabilities, and average points, with completed matches locked in.

Instructions

Monte Carlo one group within the full 12-group qualification context.

Args: group: Group letter A-L (also accepts "Group A"). iterations: Number of simulations (clamped to 100..20000).

Returns: data.teams: Per-team position probabilities, p_auto_advance, p_best_third_advance, truthful combined p_advance, and avg_points. data.iterations: iterations actually run. meta.estimated: true. meta.conditioned_matches: completed matches locked in.

Input Schema

NameRequiredDescriptionDefault
groupYesGroup letter A-L (also accepts "Group A").
iterationsNoNumber of simulations (clamped to 100..20000).

Output Schema

NameRequiredDescriptionDefault
dataNo
metaNo
errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.4.0
    • changedInput schema / properties / group / description
      Previous value: -"Group letter A-L."New value: +"Group letter A-L (also accepts \"Group A\")."
  2. First observedv0.3.1

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so the safety bar is low, but the description adds real behavioral context: results are estimates (meta.estimated), completed matches are locked in (meta.conditioned_matches), and iterations are clamped to 100..20000. These traits are not derivable from the annotations.

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

Conciseness4/5

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

Front-loaded with the core purpose in one sentence, then cleanly separated Args/Returns blocks. Slightly verbose in repeating schema text, but every line is dense and scannable with no filler.

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?

With only two parameters and an output schema present, the description is nearly complete; it even explains the semantics of meta.estimated and meta.conditioned_matches rather than leaving them opaque. The remaining gap is the lack of routing guidance relative to sibling simulation tools.

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

Parameters3/5

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

Schema description coverage is 100% and the Args section restates the same two parameter descriptions verbatim ('Group letter A-L', 'clamped to 100..20000'). It adds no meaning beyond the schema, so the baseline 3 applies.

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

Purpose4/5

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

States a specific verb+resource: a Monte Carlo simulation of one group within the 12-group qualification context. The scope ('one group' vs the full 12-group context) implicitly separates it from football_simulate_bracket and football_what_if, though no sibling is named explicitly.

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

Usage Guidelines3/5

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

Usage is only implied by the scope wording; there is no explicit 'use this when…' or 'use X instead for…' The agent must infer that per-group simulation is the alternative to the bracket-level sibling tool, and no prerequisites (e.g., needing group data loaded first) are stated.

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