get_model_leaderboard

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description does not state whether this is a read-only operation, whether it requires authentication, what data it returns, or any side effects. The mention of 'tournament PerformanceStore' and 'BEST_BASELINE' hints at internal data sources but does not explain behavior. For a tool with no annotations, this is a significant gap.

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 very short (one sentence), which is concise, but it is under-specified rather than efficiently informative. The parenthetical '（tournament PerformanceStore，含 BEST_BASELINE）' adds jargon without explaining meaning. It is not front-loaded with the most important information because the core action is missing.

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?

The tool has 2 parameters, no output schema, no annotations, and a description that does not explain the operation, parameters, or return value. Sibling tools suggest a complex domain (model performance, backtesting, validation), but this description provides no context for an agent to select or invoke the tool correctly. The description is completely inadequate for a tool with undocumented 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%, and the description does not explain the 'target' and 'horizon' parameters at all. The description only mentions the leaderboard concept and BEST_BASELINE, which does not map to the parameters. With two parameters completely undocumented in both the schema and the description, the agent has no way to know what values to provide. This is a critical gap.

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 is a noun phrase in Chinese: '模型 leaderboard（tournament PerformanceStore，含 BEST_BASELINE）' which translates to 'model leaderboard (tournament PerformanceStore, including BEST_BASELINE)'. It identifies the resource (model leaderboard) but lacks a verb stating what the tool does (e.g., 'retrieve', 'list', 'get'). The parenthetical mentions a tournament PerformanceStore and BEST_BASELINE, which is cryptic and does not clarify the operation. It does not distinguish itself from sibling tools like get_model_performance or get_analysis_packet.

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?

There is no guidance on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions. Sibling tools like get_model_performance and get_analysis_packet exist, but the description does not explain how this leaderboard tool differs from them. The only hint is the parenthetical about tournament PerformanceStore and BEST_BASELINE, which is too vague to serve as usage guidance.

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