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by net.crawlora
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nba_draft_combine_anthro

Read-only

Get NBA draft combine anthropometric measurements. Returns one draft class's combine anthropometric measurements (the DraftCombinePlayerAnthro table). Verified columns are PLAYER_ID, FIRST_NAME, LAST_NAME, POSITION, HEIGHT_WO_SHOES, WEIGHT, WINGSPAN, STANDING_REACH, HAND_LENGTH and HAND_WIDTH. The draft year is required: the endpoint has no all-years mode that was verified. Use it when comparing a draft class's physical measurements before or alongside nba-draft-combine-stats-style scouting reads; pass the four-digit draft year, for example 2025.

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": "nba_draft_combine_anthro"
  }
}

Input Schema

NameRequiredDescriptionDefault
league_idNoOptional league id; only 00 (NBA) is verified.
season_yearNoRequired four-digit draft year, for example 2025.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover safety (readOnlyHint) and scope (openWorldHint), and the description adds real context beyond them: results are scoped to a single draft class, the underlying table is named, and the absence of an all-years mode is disclosed. It does not mention ordering, row volume, or empty-result behavior for an unknown year.

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?

Front-loaded with purpose, then constraint, then usage, then the example value — a sensible order with no filler sentences. The enumerated column list is somewhat verbose and overlaps with the output schema, but it does convey which columns are verified.

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 an output schema present, return values need no explanation, and annotations cover the read-only profile; the description supplies the missing piece — that a draft year is effectively mandatory despite the schema's empty required list. Only minor gaps remain (behavior on an unverified year, ordering).

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%, so both parameters are already documented in the input schema. The description restates that season_year is a four-digit draft year (e.g. 2025) and that only league 00 is verified, which largely duplicates the schema text rather than adding new meaning.

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?

States a specific verb and resource ('Get NBA draft combine anthropometric measurements') and pins it to a named source table (DraftCombinePlayerAnthro), so it is clearly distinguishable from box-score or draft-pick siblings. An agent can identify what this returns without opening the schema.

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?

Explicitly frames the use case ('comparing a draft class's physical measurements before or alongside ... scouting reads') and warns that the draft year is required because no all-years mode is verified. It stops short of naming an explicit alternative or exclusion, so it is clear context rather than a full when/when-not routing rule.

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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