list_mlc_leaderboards

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

No annotations provided, so description bears full burden. Discloses 'Sample-size floors enforced' as a behavioral trait. Lists many aspects to show breadth, but does not mention read-only nature or any permission requirements.

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?

Front-loaded with key purpose and then illustrates breadth with many examples. Examples are relevant but could be trimmed; still efficient overall.

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?

No output schema, but description covers purpose, parameter defaults, and behavioral constraint (sample-size floors). Missing return format details, but for a simple list tool this is adequate.

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 coverage is 100%, but description adds value by specifying default (20) and max (100) for limit, and provides concrete examples for aspect like 'orange-cap'. This goes beyond the schema's generic description.

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?

Description clearly states 'Top-N rows of one MLC leaderboard aspect' with specific verb 'list' and resource 'leaderboard aspects'. It lists 55 example aspects, distinguishing it from sibling tools that focus on other leagues or general summaries.

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 recommends 'Call get_mlc_dataset_summary for the full list' and mentions 'Sample-size floors enforced'. Provides context on when to use (single aspect, top-N) but lacks explicit exclusion of multiple aspects or comparison to other leaderboard tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.