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HSH Data-on-Demand

by io.github.hshintelligence
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hsh-cricket-chase-winprob

Win probability for the side chasing in a men's T20, from the match state you send. On 106 men's T20 internationals played after all its training data (1 Jul to 17 Sep 2026): AUC 0.978 with the ground named, 0.976 without. Per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueNoThe ground's name as scorecards write it; the answer says which ground was used.
targetYesTarget to win (1st innings total + 1).
wicketsYesWickets lost (0-10).
cum_runsYesRuns scored so far.
balls_bowledYesLegal balls bowled, as a scorecard counts them: 0-120, and 12.3 overs = 75.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / balls_bowled / description
      Previous value: -"Legal balls bowled in the chase (0-120)."New value: +"Legal balls bowled, as a scorecard counts them: 0-120, and 12.3 overs = 75."
    • addedInput schema / properties / venue
      Added value: +{
+  "description": "The ground's name as scorecards write it; the answer says which ground was used.",
+  "type": "string"
+}
  2. Added

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description carries the burden of behavioral disclosure. It adds useful context: evaluation AUC, training-data cutoff, the small effect of naming the ground, and a per-call cost note. However, it does not state the output format or explicitly confirm that this is a stateless read-only prediction, though 'from the match state you send' implies it.

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 short and front-loaded with the core purpose, followed by a concise performance statement and cost note. Every sentence contributes, though the performance data could be seen as secondary rather than essential.

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?

For a simple prediction tool, the description conveys enough context: the model type, input state, validation performance, and venue handling. The absence of an output schema means the exact response shape is not guaranteed, but 'win probability' plus the venue parameter's note that 'the answer says which ground was used' gives reasonable expectations.

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 schema already explains each parameter including target, wickets, and the balls_bowled over-count convention. The description adds no new parameter-level meaning beyond reinforcing that the match state is the input, so the baseline 3 applies.

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?

The description names a specific verb and resource: computing win probability for the side chasing in a men's T20 from a given match state. This is immediately distinguishable from siblings like hsh-cricket-first-winprob and hsh-cricket-chase-difficulty.

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?

The phrase 'for the side chasing in a men's T20' provides clear context for when the tool applies, and the optional ground parameter signals when venue data matters. It does not explicitly name alternatives or exclusions, but the intended use case is unambiguous.

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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