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DanielTomaro13

sportsdata-mcp

balldontlie_nba_teams

Read-onlyIdempotent

Fetch all 30 NBA franchises with conference and division details.

Instructions

All 30 NBA franchises with conference and division.

Returns: {data:[{id, conference, division, city, name, full_name, abbreviation}]} — SHAPE FROM VENDOR DOCS.

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: Every NBA team

Auth: needs your own key in BALLDONTLIE_API_KEY.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context: it warns that the response shape is unverified from vendor docs and that an API key is required. This goes beyond the annotations to inform the agent about data reliability and auth dependencies.

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 well-structured and front-loaded with the main purpose, followed by the return shape, a reliability note, example, and auth. Each sentence serves a clear purpose, and there is no wasted content.

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?

For a simple parameterless list tool with no output schema, the description provides a complete picture: the exact team list, fields, authentication requirement, and a caution about unverified data. It even includes an example, making it fully self-contained for an agent to use correctly.

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 input schema has zero parameters, so the description does not need to explain any. The baseline for no parameters is 4, and the description still adds context about the required API key, which is relevant. It fully covers what is needed to invoke the tool.

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 resource (NBA franchises) and the scope (all 30 teams with conference and division). It also lists the exact fields returned, distinguishing it from sibling tools like balldontlie_nba_players or balldontlie_nba_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 includes an example ('Every NBA team') which implies when to use the tool, but it does not explicitly state when to use it over alternatives or provide exclusions. The usage context is clear but implicit, so a score of 3 is appropriate.

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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