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Khavel

NBA Prop Lab MCP Server

by Khavel

Get Player Research

proplab_player_research
Read-onlyIdempotent

Retrieve deep player research for NBA/WNBA props: season averages, recent game logs, matchup history, DvP, trends, and injury status using a playerId.

Instructions

Deep research data for a player: season averages, recent game logs, matchup history, DvP, trends, and injury status. Use proplab_search_players first to get the playerId.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
playerIdYesInternal player ID (from search_players)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is fully covered. The description adds useful context about what data is returned and the ID dependency, but says nothing about response shape, rate limits, or data freshness beyond what annotations provide.

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?

Two sentences with zero waste. The first sentence front-loads the data scope; the second delivers the critical prerequisite. Ideal length for a simple lookup tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only, single-parameter, idempotent tool with annotations covering safety and no output schema, the description is sufficient. It tells the agent what it returns, how to get the parameter, and that the operation is a safe read. No significant gaps.

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%, so the single playerId parameter is already fully documented in the schema (internal ID, exclusiveMinimum 0, sourced from search_players). The description only restates the search_players dependency, adding no schema-level detail.

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+resource (get player research) and enumerates the data dimensions returned (season averages, game logs, matchup history, DvP, trends, injury status). Differentiates from sibling proplab_search_players by contrasting its ID-based lookup against the search tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states the precondition: 'Use proplab_search_players first to get the playerId.' This is a clear when-to-use instruction that routes the agent to the correct sibling for ID resolution, leaving no inference needed.

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