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NBA Prop Lab

by io.github.Khavel
OverviewDiscussionsAdmin

Search players

search_players
Read-onlyIdempotent

Find NBA and WNBA players by name; returns their ids for player_research.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_players"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesPlayer name or part of it (2-50 characters).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real value beyond them by disclosing scope (NBA and WNBA) and the return shape ('their ids'), which the annotations don't provide. It stops short of noting partial-match behavior or result limits.

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 tight sentence, front-loaded with the verb and scope, with the downstream routing clause placed last. No filler whatsoever.

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 one-param lookup with no output schema, the description says enough: what it searches, across which leagues, and what comes back (ids). A note on partial matching or expected result volume would close the remaining 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 single 'query' param is fully documented with length bounds. The description's 'by name' is consistent but adds no syntax or matching-rule detail 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 ('Find') and resource ('NBA and WNBA players by name'), which is enough to distinguish it from player_research and the stats-oriented siblings. It also names the downstream consumer of the result, but doesn't explicitly contrast itself with player_research.

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?

'returns their ids for player_research' routes the agent clearly: search first, then research. That's a concrete usage path. What's missing is any when-not guidance (e.g., if you already have a player id, skip this and go straight to player_research).

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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