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by io.github.hshintelligence
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hsh-cricket-timeline

Win-probability timeline (the broadcast worm) for a men's T20 chase: one point per match state you send, from the same model as hsh-cricket-chase-winprob. Per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueNoThe ground's name, applied to every state.
eventsYes1 to 400 match states [{balls_bowled, wickets, cum_runs}], one point back per state, in the order sent; balls_bowled is legal balls (12.3 overs = 75). A `target` inside a state may be left out; if sent, it must equal the call's.
targetYesTarget to win (1st innings total + 1).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / events / description
      Previous value: -"Per-over states [{balls_bowled, wickets, cum_runs, target}]."New value: +"1 to 400 match states [{balls_bowled, wickets, cum_runs}], one point back per state, in the order sent; balls_bowled is legal balls (12.3 overs = 75). A `target` inside a state may be left out; if sent, it must equal the call's."
    • changedInput schema / properties / target / description
      Previous value: -""New value: +"Target to win (1st innings total + 1)."
    • addedInput schema / properties / venue
      Added value: +{
+  "description": "The ground's name, applied to every state.",
+  "type": "string"
+}
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the core behavior: one output point per input state, in the order sent, limited to men's T20 chases and computed by the same model as the sibling tool. It does not describe exact response format or error behavior, but 'one point per match state' is a substantive behavioral disclosure for a stateless computation.

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?

One dense sentence with the core object front-loaded ('Win-probability timeline'), scope defined, model provenance included, and a terse 'Per call.' closer. Every phrase earns its place and there is 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?

For a simple 3-param tool with no output schema and no annotations, the description is nearly complete: it states the output shape ('one point per state'), ordering, scope, and model provenance. It only leaves the exact response format and the explicit single-state alternative slightly implicit, which is a minor gap.

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 coverage is 100% and the schema already documents every parameter richly, including events structure, legal-balls formula (12.3 overs = 75), target meaning, and venue application. The description does not add parameter-specific meaning beyond the schema, so the baseline of 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 states a specific verb and resource: 'Win-probability timeline (the broadcast worm) for a men's T20 chase' with 'one point per match state you send.' It also names the sibling model it derives from (hsh-cricket-chase-winprob), helping an agent distinguish it from related cricket tools.

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 description gives clear context: this is the timeline variant, producing one point per match state sent, and it explicitly ties itself to hsh-cricket-chase-winprob as the source model. It does not explicitly state 'use chase-winprob for a single state' or list exclusions, but the per-call/per-state phrasing makes the intended use apparent.

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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