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Sports Hub MCP Server

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TypeScript
Hybrid

ncaa: Get stats

ncaa_get_stats
Read-onlyIdempotent

Retrieve player or team statistics for any NCAA sport and division. Specify year, sport, division, and stat type to get the exact college stats you need.

Instructions

Get player or team statistics for an NCAA sport/division.

Input Schema

NameRequiredDescriptionDefault
yearNoYear (e.g. 2024)
sportYesSport name, e.g. "football", "basketball"
fieldsNoKeys to keep (comma-separated).
divisionYesDivision code, e.g. "fbs", "d1"
stat_typeNoStat type: "individual" or "team"

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.4.1
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • addedInput schema / properties / fields
      Added value: +{
+  "description": "Keys to keep (comma-separated).",
+  "type": "string"
+}
  2. Changed1 schema field changedv1.3.0
    • removedInput schema / additionalProperties
      Removed value: -false
  3. First observedv1.2.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint, so the safety/idempotency profile is covered. The description adds nothing beyond that — no note on what stats are returned, whether year defaults to the current season when omitted, or what happens if stat_type is left out. For a 5-param tool with no output schema, that is thin.

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?

A single front-loaded sentence with no filler or repetition. It is efficient, though its brevity is also the source of the missing usage and behavior detail rather than being deliberately tight.

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?

Parameters are fully documented in the schema, but with no output schema and no annotations about response shape, the description leaves unclear what a 'statistics' payload contains and how the optional stat_type/year omission is handled. Minimum viable, not complete for a 5-param stats query tool.

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 year, sport, fields, division and stat_type with examples and defaults. The description only echoes the player/team distinction already captured by stat_type. Baseline 3 is appropriate; it adds no format or constraint detail beyond the schema.

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 ('Get player or team statistics') and scopes it to NCAA sport/division, so the agent knows exactly what data comes back. It does not differentiate from close siblings like ncaa_get_standings or ncaa_get_rankings, which also return aggregate NCAA data — a reader must infer the split from the stat_type parameter.

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?

No when-to-use, when-not-to-use, or alternative routing is given. In a family with ncaa_get_standings, ncaa_get_rankings and ncaa_get_game, the description offers no signal for choosing this tool over those.

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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