predict_lottery

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. It states the tool generates predictions based on historical data, but doesn't describe what the predictions look like (e.g., format, structure), whether it's a read-only operation, potential rate limits, accuracy claims, or how it interacts with data sources. For a prediction tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence: '预测彩票号码，基于历史数据生成预测结果'. It's front-loaded with the core purpose and avoids unnecessary words. However, it could be slightly more structured by separating the prediction action from the data source for clarity.

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 complexity (a prediction tool with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain the prediction output format, accuracy, data freshness, or how it differs from sibling tools. For a tool that generates predictions based on historical data, more context is needed to guide effective use.

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 100%, with all parameters well-documented in the schema itself (e.g., 'lottery_type' with enum values, 'method' with only 'rule', 'count' with range 1-20, 'strategy' with enum options). The description adds no additional meaning beyond what's in the schema—it doesn't explain what 'rule' method entails, what the strategies mean, or how parameters interact. Baseline 3 is appropriate when the schema does the heavy lifting.

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 the tool's purpose: '预测彩票号码，基于历史数据生成预测结果' (Predict lottery numbers, generate prediction results based on historical data). It specifies the verb ('预测' - predict) and resource ('彩票号码' - lottery numbers), and mentions the data source ('历史数据' - historical data). However, it doesn't explicitly differentiate from sibling tools like 'generate_random_numbers' or 'analyze_numbers' which might have overlapping functionality.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, when-not-to-use scenarios, or comparisons with sibling tools like 'generate_random_numbers' (which might generate numbers without historical data) or 'backtest_lottery' (which might evaluate predictions). The agent must infer usage solely from the tool name and parameters.

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