Statcast MCP Server

player_lookup

Find MLB player IDs and career details by entering a player's name. Use this tool to verify identities and obtain database identifiers for subsequent baseball data queries.

Instructions

Look up a baseball player to find their MLBAM ID, years active, and database IDs.

Accepts names like 'Mike Trout', 'Trout, Mike', or 'Shohei Ohtani'. Useful for verifying a player's identity or finding their MLBAM / FanGraphs / Baseball-Reference IDs before running other queries.

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": {
      "player_name": "<player_name>"
    },
    "name": "player_lookup"
  }
}

Input Schema

Name	Required	Description	Default
`player_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Mar 26, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses input formats and return values but lacks explicit safety declarations (read-only status), error handling details, or rate limiting information that would help an agent understand operational constraints.

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?

Three well-structured sentences with zero waste: purpose/outputs first, input formats second, usage context third. Every sentence earns its place and follows logical progression.

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?

Given the existence of an output schema and simple single-parameter input, the description is appropriately complete. It identifies the specific database IDs returned (MLBAM, FanGraphs, Baseball-Reference) which contextualizes the tool within the baseball data ecosystem.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage (only 'Player Name' as title). The description compensates effectively by providing concrete input examples ('Mike Trout', 'Trout, Mike', 'Shohei Ohtani'), clarifying the expected name formats beyond the bare 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?

The description clearly states the specific action (look up), resource (baseball player), and outputs (MLBAM ID, years active, database IDs). It effectively distinguishes this identity-resolution tool from its sibling statistics tools.

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?

The description provides clear guidance on when to use the tool ('before running other queries'), positioning it as a prerequisite for the sibling stats tools. However, it does not explicitly name specific alternatives or state when NOT to use it.

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