Fuzzy player-name search (typo-tolerant). Currently NBA only — for other leagues query the players table with a name filter via query_table. Returns player rows ordered by match quality. Requires an API key; rows count against quota.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| limit | No | ||
| league | No | nba |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses typo tolerance, NBA-only scope, ordering by match quality, API key requirement, and quota accounting. This is substantial behavioral context, though it stops short of detailing pagination or error behavior.
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?
Four sentences, each focused and informative. The opening phrase is immediately descriptive, and the rest adds necessary caveats without redundancy.
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 tool's simplicity and lack of output schema/annotations, the description covers purpose, scope, ordering, and quota. It does not specify exact row fields or error scenarios, but these are not critical for an agent's basic invocation decision.
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?
Schema coverage is 0%, so the description must compensate. It clarifies the 'name' parameter via 'Fuzzy player-name search' and the 'league' parameter via 'NBA only', but it does not explain the 'limit' parameter or its effect on quota and result count. Partial compensation leaves a gap.
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 'Fuzzy player-name search (typo-tolerant)' uses a specific verb and resource and immediately conveys the core function. It also distinguishes from sibling tools by explicitly stating 'for other leagues query the players table with a name filter via query_table'.
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?
It explicitly states when to use the tool ('Currently NBA only') and provides a concrete alternative ('for other leagues query the players table with a name filter via query_table'). This gives clear guidance on both usage and 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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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.
