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baseball-stats-mcp

baseball-stats-mcp

by HPotty36
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

MLB standings with Pythagorean record

mlb_standings
Read-onlyIdempotent

Fetch regular-season MLB standings by division, including run differential, Pythagorean expected wins, and luck (actual wins minus expected), as markdown or JSON.

Instructions

Regular-season standings by division, with run differential, Pythagorean expected wins, and luck (actual wins minus expected).

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": "mlb_standings"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNo'markdown' (default) or 'json'.markdown
seasonNoSeason year. Default: current season.
exponentNoPythagorean exponent (1.83 is the common choice; 2.0 is Bill James' original).

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and openWorld, so the safety profile is covered. The description adds useful content context (what metrics are computed) but says nothing about data freshness, season availability, or any rate/API constraints, so it adds only modest value beyond the structured fields.

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?

A single front-loaded sentence naming the resource and its key outputs, with zero filler. Nothing redundant with the title or schema.

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?

An output schema exists, so return values need not be explained, and the schema fully documents all three parameters. The description covers scope and computed metrics; only minor omissions (e.g., whether standings are live or cached) keep it from a 5.

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%, with 'format', 'season' and 'exponent' fully documented including defaults and ranges, so the baseline of 3 applies. The description alludes to Pythagorean expected wins but adds no new syntax or usage meaning for the exponent parameter beyond what the schema states.

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?

States a specific verb+resource ('Regular-season standings by division') and enumerates the derived metrics returned (run differential, Pythagorean expected wins, luck), so the agent knows exactly what it gets. It does not explicitly differentiate itself from siblings like baseball_leaderboard, which could partially overlap, so it stops short of a 5.

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?

Usage is only implied: an agent can infer this is the tool for division standings, but there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., vs baseball_leaderboard). Minimum viable guidance, no exclusions.

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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