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Ninjabeam20

SportIQ

by Ninjabeam20
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

football_xg_model

Read-onlyIdempotent

Estimate expected goals and win/draw/loss probabilities for a football match from home and away teams, with an option to remove home advantage for neutral venues.

Instructions

Estimate a match's expected goals and win/draw/loss probabilities.

Args: home_team: First team, FIFA code or name (e.g. "ARG" or "Argentina"). away_team: Second team, FIFA code or name (e.g. "BRA" or "Brazil"). neutral: True for a neutral venue (no home advantage). World Cup default.

Returns: data: {expected_home_goals, expected_away_goals, home_win, draw, away_win}. meta.estimated: true.

Input Schema

NameRequiredDescriptionDefault
neutralNoTrue for a neutral venue (no home advantage). World Cup default.
away_teamYesSecond team, FIFA code or name (e.g. "BRA" or "Brazil").
home_teamYesFirst team, FIFA code or name (e.g. "ARG" or "Argentina").

Output Schema

NameRequiredDescriptionDefault
dataNo
metaNo
errorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.4.0
    • changedInput schema / properties / away_team / description
      Previous value: -"Second team code (e.g. \"BRA\")."New value: +"Second team, FIFA code or name (e.g. \"BRA\" or \"Brazil\")."
    • changedInput schema / properties / home_team / description
      Previous value: -"First team code (e.g. \"ARG\")."New value: +"First team, FIFA code or name (e.g. \"ARG\" or \"Argentina\")."
  2. First observedv0.3.1

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds that results include meta.estimated=true, but does not explain model limitations, assumptions, or when estimates may be unreliable.

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?

The description is structured and front-loaded, with purpose first and Args/Returns sections afterward. It has minor redundancy with the schema but no wasted prose.

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 annotations and an output schema present, the description is sufficient to invoke the tool correctly and understand the returned fields. The main remaining gap is lack of sibling differentiation, which affects selection more than invocation.

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 description repeats the same parameter meanings and examples already present in the schema. It adds no meaningful semantics beyond what the schema provides, so the baseline 3 is appropriate.

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?

The description states a specific action and output set: estimating expected goals and win/draw/loss probabilities for a match. It is clear what the tool does, but it does not distinguish itself from close siblings such as football_match_predictor or football_what_if.

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

Usage Guidelines2/5

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

It gives no explicit guidance on when to use this tool versus alternatives like football_match_predictor. The note about neutral venue is parameter context, not usage guidance or exclusions.

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

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