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nba_player_game_logs

Retrieve a player's game-by-game box-score log for a season, with one row per game, to review per-game performance statistics.

Instructions

Returns one player's game log for a season, one row per game with the player's box-score line. Returns one player's game log for a season, one row per game with the player's box-score line.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "player_id": "<player_id>"
    },
    "name": "nba_player_game_logs"
  }
}

Input Schema

Name	Required	Description
`season`	No	Optional season in YYYY-YY form; defaults to the current NBA season.
`league_id`	No	Optional league id; only 00 (NBA) is verified.
`player_id`	Yes	Required player id from nba-players or nba-team-roster.
`season_type`	No	Optional season type.

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.17.20Nov 12, 2026

TDQS

B3.1/5.0

Behavior3/5

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

No annotations exist, so the description carries the full burden. 'Returns' signals a read-only operation and 'one row per game with the box-score line' discloses the return granularity, which is the most useful piece of behavior here. Nothing is said about row volume, pagination, or whether missing players yield an empty list or an error.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The single sentence is repeated verbatim, a copy-paste artifact that doubles the length without adding information. The content itself is front-loaded and clear, but the duplication is exactly the kind of waste this dimension penalizes.

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?

For a four-parameter read tool with fully documented parameters and no output schema, the description covers what the tool returns and at what granularity, which is the key missing piece such tools usually omit. The absence of any sibling routing or volume expectations is the remaining gap.

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

Parameters3/5

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

Schema coverage is 100%, so all four parameters including the season default and the enum options are already documented in structured form. The description only echoes 'for a season' and adds no meaning beyond the schema, so the baseline of 3 applies.

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?

The description states a specific verb and resource: 'Returns one player's game log for a season, one row per game with the player's box-score line.' The per-player scope plus the row granularity makes it distinguishable in spirit from nba_team_game_logs and nba_league_game_log, but it never names an alternative, so differentiation is inferred 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 Guidelines2/5

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

There is no when-to-use guidance, no when-not-to-use, and no named alternative. An agent must guess whether this or nba_player_stats / nba_league_game_log is the right call for game-level box scores.

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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