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stat-api — Sports Data

by com.stat-api

query_table

Query any table. Returns {"<table>": [rows], "limit": N, "next_from_id": M|null}; page by re-calling with from_id set to the previous next_from_id until it is null. filters is a flat object of field or field__op keys from describe_table (e.g. {"season_id": 2024, "day__between": "20250101,20250131"}). Requires an API key (Authorization: Bearer ; free tier at https://stat-api.com). Every returned row counts against the monthly record quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
tableYes
leagueYes
filtersNofield or field__op keys → scalar values; see describe_table
from_idNokeyset cursor from next_from_id

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description fully discloses return format, pagination behavior, filter structure, API key requirement, and monthly quota impact. This is comprehensive behavioral transparency for a query tool.

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 dense but compact, with every sentence contributing critical information: return format, pagination, filters, auth, and quota. It is front-loaded and well-structured.

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?

Despite no output schema, the description covers return format, pagination, filter syntax, authentication, and quota—making it complete for a general-purpose query tool. It appropriately references describe_table for filter operators without overloading.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 40%, but the description compensates by explaining the `filters` object format with an example, the `from_id` pagination cursor, and the `limit` field in the return. It adds meaning beyond the schema for the least obvious parameters.

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 starts with 'Query any table,' a specific verb+resource that clearly states the tool's function. It distinguishes itself from siblings like describe_table (schema), get_record (single record), and graphql_query (alternative query mechanism).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly implies this is the general-purpose table query tool and references describe_table for filter syntax. It lacks explicit when-not-to-use or named alternatives, but the pagination and filter context make the usage clear.

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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TDQS

A4.4/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

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