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DanielTomaro13

sportsdata-mcp

by DanielTomaro13
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

nbl_match_playbyplay

Read-onlyIdempotent

Retrieve one NBL game's full play-by-play by match ID, including period, clock, player, shot outcome, coordinates, and running score.

Instructions

One game's full play-by-play: every event with period, game clock, shot clock, type, the player, success on shots, shot coordinates and the running score. Large: ~1,000 events for a game. Get the match id from nbl_schedule (data[].id).

Returns: {type, count, data:[{id, match_title, status, home_score, away_score, home_team:{name, team_code}, away_team:{…}, play_by_play:[{action_id, period, period_type, clock:'02:12', shot_clock, action_type:'2pt'|'3pt'|'freeThrow'|'rebound'|'foul'|'substitution'|'possession'|…, readable_action_type:'2-Point Shot - Driving Layup', success, score_1, score_2, coordinates:{x, y}, team:{team_code}, player:{full_name, jersey_number}, timestamp}]}]} — score_1 is the HOME score and score_2 the away score. success is set on shots only. player is absent on team events (possession, period markers). Free throws are freeThrow, camel-cased. About 40% of events are possession and clock bookkeeping, not plays. score_1/score_2 are integers, but the header home_score/away_score are STRINGS.

Example: Tasmania JackJumpers v NZ Breakers, NBL Blitz, 6 Sep 2026 {"matchId": "3d09795f-58a7-11f1-aed3-b7a799a5060e"}

Auth: none needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
matchIdYesMatch UUID from nbl_schedule (data[].id). Required — part of the URL path.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.33.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent behavior, and the description adds substantial context beyond them: response size (~1,000 events), that ~40% of events are possession/clock bookkeeping not plays, that `success` is only set on shots, that `player` is absent on team events, and that header scores are strings while score_1/score_2 are integers. This is exactly the kind of parsing-critical detail annotations cannot convey.

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?

Front-loaded with the core purpose and size warning before the return-shape detail. The inline return schema is dense but each field note earns its place given the lack of an output schema; it is longer than strictly necessary but not padded.

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?

With no output schema, the description must explain return values, and it does so thoroughly — field meanings, type quirks, missing-field semantics, and an example call. Nothing an agent needs to correctly parse the response 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% and the single parameter is documented there, so the schema does the heavy lifting. The description reinforces the source of the id (nbl_schedule) and adds that it is 'part of the URL path', giving modest extra value above the baseline.

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?

States a specific verb and resource ('full play-by-play: every event with period, game clock, shot clock, type, the player...') and clearly distinguishes itself from siblings like nbl_match_boxscore by emphasizing event-level granularity. An agent can tell what this returns without opening the schema.

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?

Provides a clear prerequisite ('Get the match id from nbl_schedule (data[].id)') and warns about output size ('Large: ~1,000 events for a game'), which guides invocation. It does not explicitly state when to choose this over nbl_match_boxscore, so it stops short of naming alternatives.

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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