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DanielTomaro13

sportsdata-mcp

mlb_team_roster

Read-onlyIdempotent

Retrieve a major league baseball team's roster with player positions, jersey numbers, and status. Select roster type: active, 40-man, full-season, or depth chart.

Instructions

A team's roster — players with position and jersey number. rosterType selects active / 40-man / full-season / depth chart.

Returns: {roster:[{person:{id, fullName}, jerseyNumber, position:{abbreviation, name}, status}], teamId, rosterType}

Auth: none needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoRoster as of a date (YYYY-MM-DD).
seasonNoSeason year.
teamIdYesTeam id (from mlb_teams). Required — part of the URL path.
hydrateNoEmbed related objects (e.g. 'person(stats(type=season))').
rosterTypeNoactive | 40Man | fullSeason | fullRoster | depthChart | gameday.active
Behavior4/5

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

Annotations already declare read-only, open-world, and idempotent behavior. The description adds value beyond that by disclosing the exact return structure and explicitly stating no authentication is needed, which is useful context for an agent.

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?

The description is compact and well-organized, with a clear opening statement, a separate Returns block, and an Auth note. Every sentence contributes useful information with no filler.

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?

Since there is no output schema, the description compensates by providing the return format. Combined with the full input schema and read-only annotations, this gives an agent enough information to select and invoke the tool correctly.

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 the description doesn't need to restate parameter meanings. It does mention rosterType values but omits some options listed in the schema (fullRoster, gameday) and uses slightly different naming ('full-season' vs 'fullSeason'), adding no real semantic value beyond the schema.

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?

Description clearly identifies the tool as returning a team's roster with players, positions, and jersey numbers. It also distinguishes between roster types (active, 40-man, full-season, depth chart), which separates it from sibling tools like team coaches or personnel.

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?

The description implies the tool is used when you need roster data and explains rosterType options, but it does not explicitly state when to use this tool over alternatives (e.g., mlb_team, mlb_team_coaches). No exclusions or 'use instead' guidance is given.

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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