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mlb_statcast_oaa

Retrieve Baseball Savant Outs Above Average leaderboards by fielder, team, batter, or pitcher, filtering by season, split, position, attempts, and sort order.

Instructions

Get Baseball Savant Outs Above Average. Returns the separate Outs Above Average leaderboard for fielders, fielding teams, batters, batting teams, or pitchers. Supports season range, split years, team, monthly range, attempts, position, detailed fielder roles, local sorting, and pagination.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoSort field
typeNoLeaderboard view
limitNoRows per page (1-500)
rangeNoTime range
rolesNoComma-separated detailed fielder role ids. Values: 32,30,31,77,71,70,72,78,43,42,40,41,46,87,81,82,89,64,62,60,61,98,91,90,92,99,51,50,52
splitNoReturn one row per season in a year range
offsetNoZero-based row offset
minimumNoMinimum attempts
team_idNoOptional MLB team id from mlb_teams
end_yearNoEnd season from 2016 through the current season
positionNoPosition filter
sort_dirNoSort direction
start_yearNoStart season from 2016 through the current season

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.7

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that five distinct leaderboard views exist and that pagination/sorting/splitting are supported, which is real context, but it says nothing about auth, rate limits, or return volume.

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?

Two sentences, front-loaded with what the tool returns and then the supported dimensions. Dense and waste-free, though the second sentence is essentially a parameter roster.

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

Completeness3/5

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

For a 13-parameter tool with no output schema and no annotations, the description gives an adequate but not rich overview. It never describes the shape or columns of the returned leaderboards, leaving the agent to discover results empirically.

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 schema already documents all 13 parameters and their enums. The description restates the parameter categories (season range, split years, team, attempts, position, roles, sorting, pagination) but adds no format or syntax meaning beyond what the schema provides.

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 data product (Baseball Savant Outs Above Average) and enumerates the leaderboard variants it returns (fielders, fielding teams, batters, batting teams, pitchers). An agent can tell what this tool produces, though it never differentiates itself from siblings like mlb_statcast or mlb_statcast_expected.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no indication of how this differs from mlb_statcast or mlb_statcast_expected, and no prerequisites or exclusions. The mode list implies usage but the agent must infer which sibling to pick.

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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