statsPer-game NBA player box-score stats filtered by player, team, game, or season. Returns PTS, REB, AST, STL, BLK, TO, FG%, FT%, 3P%, minutes, +/-. Use for individual game performance lookup, box-score analysis, fantasy stats.
| Name | Required | Description | Default |
|---|---|---|---|
| cursor | No | ||
| seasons | No | ||
| game_ids | No | ||
| per_page | No | ||
| player_ids | No | ||
| postseason | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/false. The description adds value by listing the returned stats (PTS, REB, AST, etc.) and filter options, but does not elaborate on pagination behavior (cursor parameter), error scenarios, or rate limits. With annotations covering safety and idempotency, the description provides moderate additional context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action and return data, with no filler or redundancy. Every sentence contributes essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low schema coverage (0%), the description does not fully explain all parameters (e.g., cursor, per_page, postseason) or pagination. While output schema exists, the description should compensate for missing parameter documentation to ensure the agent can invoke the tool correctly. Current depth is insufficient for a tool with 6 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions filtering by 'player, team, game, or season', but the schema includes 'team' only implicitly (no parameter named 'team'), and lacks explanation for 'cursor', 'per_page', and 'postseason'. Only 3 of 6 parameters are partially described, leaving significant ambiguity for the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing per-game NBA player box-score stats with specific metrics (PTS, REB, AST, etc.) and filtering dimensions (player, team, game, season). This distinguishes it from sibling tools like 'player' or 'season_averages' by focusing on individual game performance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states explicit use cases ('individual game performance lookup, box-score analysis, fantasy stats'), providing clear context. However, it does not specify when to avoid this tool or mention alternatives like 'player' (for player info) or 'season_averages' (for season-level data), which would strengthen guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all performing similar data queries. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, and polymarket_fill_risk cover the same betting domain. Players/player, teams/team, and games/game also blur distinctions.
Naming styles are inconsistent: some use verb_noun (e.g., validate_claim, discover_tools), others are plain nouns (e.g., player, team, stats), and some are individual verbs (e.g., forget, recall). There's no predictable pattern.
With 39 tools, the set is large and spans multiple unrelated domains (NBA stats, betting, general data lookup, memory). Given the server name 'Balldontlie' suggests NBA focus, the number is excessive and many tools feel out of place.
For an NBA stats server, the tool surface is incomplete (missing play-by-play, advanced stats, season leaders, etc.). As a general data server, it relies on meta-tools like ask_pipeworx rather than dedicated tools, so coverage is indirect and not comprehensive.