List every league/data group this API serves (nfl, nba, mlb, nhl, pga, dfs, kalshi, polymarket, reference) with table counts and availability. No credentials needed. Start here, then call list_tables.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that no credentials are needed and that the response includes table counts and availability. It does not discuss error behavior or rate limits, but for a simple listing tool these are not critical. This goes beyond a minimal description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. It front-loads the action, provides concrete examples, states access requirements, and gives a clear next step. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (no parameters, no output schema), the description fully covers what an agent needs: what the tool does, what info is returned, authentication, and how to proceed. This is complete for its scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema trivially covers 100% of them. The description adds no parameter details because none exist. Per the rubric, a baseline of 4 is appropriate when there are no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists every league/data group served by the API, naming specific examples (nfl, nba, etc.), and mentions table counts and availability. This is a specific verb+resource that distinguishes it from siblings like list_tables.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells the agent to 'Start here, then call list_tables,' which provides clear sequential guidance. It also notes that no credentials are needed, implying it is a lightweight entry point. It could explicitly mention when not to use it or name alternatives, but the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct purpose: metadata discovery (list_leagues, list_tables, describe_table), data retrieval (query_table, get_record, graphql_query), convenience queries (games_on_date, game_markets, search_players), and account management (api_usage). No two tools have overlapping boundaries despite some sharing the ability to access data.
Most tools follow a clear verb_noun pattern (describe_table, get_record, list_leagues, list_tables, query_table, search_players), but a few use noun-only names (api_usage, game_markets, games_on_date, graphql_query). The naming is still readable and lowercase snake_case throughout, but the mix prevents a perfect score.
10 tools is well-scoped for a sports data API: it provides the essential discovery, schema inspection, querying, and retrieval operations, plus a few convenience wrappers. The count is neither too thin nor bloated.
The tool set fully covers the lifecycle of a read-only data API: exploring available leagues/tables, understanding table schemas, querying with filters and pagination, fetching by primary key, and accessing relational data via GraphQL. Convenience tools for games, markets, and player search address common use cases, and any data not directly exposed can be accessed through query_table.
