gamesNBA games filtered by season, team, or date range. Returns matchup, scores, date, season, postseason flag, status. Use for "did the Lakers win last night", schedule lookup, historical game search.
| Name | Required | Description | Default |
|---|---|---|---|
| dates | No | YYYY-MM-DD | |
| cursor | No | ||
| seasons | No | ||
| per_page | No | ||
| team_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=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds return fields but no additional behavioral details like pagination behavior or rate limits. With comprehensive annotations, the bar is lower, and the description provides adequate but not extra transparency.
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?
Two sentences: first states functionality and output, second gives use cases. No superfluous words. Information is front-loaded and clearly structured.
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 rich annotations and existence of an output schema, the description covers the essential purpose and typical use cases. Missing details about pagination or ordering are minor omissions; overall it is sufficiently complete for a read-only data retrieval tool.
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 17% (only 'dates' described). The description mentions filtering by season, team, or date range, which helps for three parameters (seasons, team_ids, dates). However, cursor, per_page, and postseason are not explained. This partially compensates for low coverage but leaves gaps.
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 specifies the tool is for NBA games filtered by season, team, or date range, and lists what fields are returned. It provides concrete use cases like 'did the Lakers win last night' and distinguishes from sibling tools like 'game' (singular) through plural naming and scope.
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 explicitly states when to use the tool (schedule lookup, historical game search) but does not mention when not to use it or compare with alternatives like the singular 'game' tool. The use cases are clear, but explicit exclusions are missing.
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.