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by net.crawlora
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nba_team_stats

Read-only

Get the league-wide NBA team statistics table and return one normalized table with column headers and one row per team. Use season in YYYY-YY form, season_type Regular Season or Playoffs, measure_type Base or Advanced and per_mode PerGame or Totals; only league_id 00 (NBA) is verified. The upstream requires the complete parameter set and answers a trimmed query with HTTP 500, so the service always sends the full set; an unverified enum value stays an upstream error rather than being guessed.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "nba_team_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
seasonNoOptional season in YYYY-YY form, for example 2026-27. Defaults to the current NBA season.
per_modeNoOptional aggregation; PerGame or Totals. Defaults to PerGame.
league_idNoOptional league id; only 00 (NBA) is verified.
season_typeNoOptional season type; Regular Season or Playoffs. Defaults to Regular Season.
measure_typeNoOptional statistic family; Base or Advanced. Defaults to Base.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

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

Beyond the readOnly/openWorld annotations the description discloses real operational behavior: the response is a normalized single table with headers, the service always sends the complete parameter set, the upstream returns HTTP 500 on trimmed queries, and an unverified enum value surfaces as an upstream error instead of being guessed. These are exactly the failure-mode and output-shape facts an agent needs and cannot get from annotations.

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?

Three sentences with no filler, and the output-shape statement is front-loaded before the parameter and upstream-behavior details. The middle sentence is a dense enumeration that could be split, but every clause carries information.

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 5-parameter read-only tool with an output schema and full schema coverage, the description covers output shape, default behavior, verified enum scope, and the upstream error semantics. Nothing an agent needs in order to call it correctly and interpret failures is missing.

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?

Schema coverage is 100%, so the baseline is 3, and the description largely repeats the enum vocabularies and YYYY-YY format already documented. It earns above baseline by adding the non-obvious semantic that the full parameter set is always transmitted regardless of what the caller omits, and that only league_id 00 is verified, which tells the agent how defaults actually behave upstream.

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?

States a specific verb and resource ('Get the league-wide NBA team statistics table') and pins down the output shape ('one normalized table with column headers and one row per team'), which distinguishes it from the team-split/dashboard siblings. It stops short of naming an alternative, so the differentiation is implicit rather than explicit.

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?

Guidance is about parameter values (season format, season_type, measure_type, per_mode, league_id 00 only) rather than about when this tool is the right choice versus nba_team_dash_by_clutch or nba_team_details. The league-wide/one-row-per-team framing implies the use case, but no explicit when-to-use or when-not-to-use statement is given.

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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