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nba_league_game_log

Read-only

Get NBA game log for a season, by team or by player. Returns every game in a season as the league's game log. With player_or_team=T each row is one team-game and carries GAME_ID, TEAM_ID, GAME_DATE, MATCHUP, WL, MIN, PTS, PLUS_MINUS and VIDEO_AVAILABLE; with player_or_team=P each row is one player-game (the same identity columns plus PLAYER_ID and PLAYER_NAME) and the payload grows to several hundred KB. direction orders the log newest-first (DESC) or chronologically (ASC).

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_league_game_log"
  }
}

Input Schema

NameRequiredDescriptionDefault
seasonNoOptional season in YYYY-YY form, for example 2026-27. Defaults to the current NBA season.
sorterNoOptional sort key; only DATE is verified. Defaults to DATE.
directionNoOptional ordering; ASC or DESC. Defaults to DESC.
league_idNoOptional league id; only 00 (NBA) is verified.
season_typeNoOptional season type; Regular Season, Playoffs, Pre Season or All Star. Defaults to Regular Season.
player_or_teamNoOptional subject; T for one row per team or P for one row per player. Defaults to T. The player log is several hundred KB.

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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds genuinely useful behavioral context beyond that: row granularity per mode, the full column set returned, and an explicit warning that the player payload grows to several hundred KB, which matters for an agent managing context.

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?

Front-loaded with the core action, then the two mode-specific output shapes, then ordering, with no filler sentences. The column enumeration is dense but each item carries information an agent needs to reason about the payload.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so the description is not obligated to document return values, yet it still previews the row shapes and payload size, which is helpful for context-budget decisions. Coverage of defaults and error/edge behavior is thin, but nothing essential for a correct call 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 would be 3, but the description adds real meaning: it explains that T yields one row per team-game and P yields one row per player-game with extra identity columns, and it spells out DESC as newest-first versus ASC as chronological. That is semantics the schema strings alone do not convey.

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 NBA game log for a season') and clarifies the two subject modes (team or player). It does not explicitly route the agent away from similarly named siblings such as nba_player_game_logs or nba_team_game_logs, so the differentiation is implied rather than stated.

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 makes clear that player_or_team selects between a team-season log and a player-season log, and that direction controls ordering, which implies the usage context. However, it never says when to prefer this league-wide log over the more specific sibling tools, nor does it state any exclusions or prerequisites.

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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