gameSingle NBA game by ID: matchup, scores, date, period, time, venue, season, postseason flag. Use after games() to fetch detail.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds the specific output fields (matchup, scores, etc.), but given the output schema exists, the added value is moderate.
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?
A single, concise sentence that front-loads the tool's purpose and usage guidance, with no redundant words.
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?
Given the tool's simplicity (one parameter), rich annotations, and existing output schema, the description fully covers what the agent needs to know for correct invocation.
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?
The description adds meaning by stating the tool works 'by ID', clarifying that the single parameter is a game identifier. With 0% schema coverage, this compensation is sufficient for a simple number parameter.
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 states it retrieves a single NBA game by ID, listing specific data fields (matchup, scores, date, etc.), which distinguishes it from the sibling tool 'games' that lists multiple games.
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?
Explicitly advises to use this tool after 'games()' to fetch detail, providing a clear workflow and contrasting with the listing tool.
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.