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DanielTomaro13

sportsdata-mcp

cfbd_advanced_box_score

Read-onlyIdempotent

Retrieve advanced metrics for a single college football game, including success rate, explosiveness, PPA, and field position, to analyze team efficiency and game context.

Instructions

Advanced box score for one game: success rate, explosiveness, PPA and field position.

Returns: {gameId, teams:{ppa:[…], cumulativePpa:[…], successRates:[…], explosiveness:[…], rushing:[…], havoc:[…], scoringOpportunities:[…], fieldPosition:[…]}} — SHAPE FROM VENDOR DOCS. PPA is CFBD's expected-points-added metric.

NOTE: this shape is from the vendor's documentation and has NOT been verified against a live response (we hold no key for this provider). Treat it as approximate — inspect the actual payload before relying on a field name.

Example: One game's advanced box {"id": 401520165}

Auth: needs your own key in CFBD_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesGame id (from cfbd_games). Passed as a QUERY param, not in the path.
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so those are covered. The description adds valuable transparency by disclosing that the return shape is 'from vendor docs' and 'NOT been verified against a live response,' as well as the auth requirement and PPA definition. This helps the agent set expectations and inspect the actual payload.

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?

The description is front-loaded with a clear one-sentence summary, followed by the return shape, caveat, example, and auth note. Each section serves a purpose and the structure is logical, though the caveat note is somewhat verbose.

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?

With no output schema, the description details the return structure and warns it is unverified. It also covers authentication and provides an example. This is a reasonably complete picture for a single-parameter tool, though it doesn't discuss error modes or edge cases.

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% and already explains that 'id' is a query parameter from cfbd_games. The description reinforces this with an example value, but it doesn't add significant new semantic information 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 clearly states 'Advanced box score for one game' and lists the key metrics (success rate, explosiveness, PPA, field position), which identifies the tool's purpose and scope. It doesn't explicitly differentiate from sibling CFBD tools, but the 'one game' constraint distinguishes it from list-style tools like cfbd_games.

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 usage via 'one game' and provides an example call, but it doesn't explicitly state when to choose this over alternatives or where to obtain the required game ID. The schema description covers the ID source, yet the tool description itself lacks that contextual guidance.

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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