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NBA Prop Lab

by io.github.Khavel
OverviewDiscussionsAdmin

NBA referee assignments today

referee_assignments_today
Read-onlyIdempotent

Officiating crew of each NBA game today (published by the league the morning of game day) with each referee's fouls per game.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "referee_assignments_today"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, closed-world, non-destructive semantics, so the description only needs to add what they cannot. It does: the freshness constraint that data is published league-side the morning of game day, which tells the agent why results may be empty or stale early in the day.

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 sentence, front-loaded with the resource, followed by the two facts that actually change agent behavior: publication timing and the included metric. Nothing is padded.

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 no parameters, no output schema, and full annotation coverage, the description carries only modest burden and meets it by disclosing scope, freshness, and the included per-referee statistic. Slightly more on the shape of the return (one entry per game vs. per referee) would fully close the gap.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The description correctly implies a parameterless 'all games today' scope rather than inviting filters.

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 names a specific resource (officiating crew per NBA game) with a clear temporal scope (today) and states the per-referee payload (fouls per game). It does not differentiate from the sibling 'referees' tool, so an agent must infer the split from the name alone.

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 implied only: 'today' scope and game-day timing suggest when the data is relevant, but there is no explicit when-to-use or when-to-prefer-'referees' guidance. The agent gets a hint of context without routing instructions.

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

All NBA Prop Lab toolsInspect
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