backtest_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 evaluates prediction accuracy through backtesting, implying it's a read-only analysis tool, but doesn't specify what the evaluation outputs (e.g., metrics like accuracy scores), whether it modifies data, requires specific permissions, or has performance constraints. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 concise with two short phrases ('回测预测算法，评估预测准确性'), making it front-loaded and efficient. Every word contributes to the purpose, with no redundant information. However, it could be slightly more structured by explicitly separating the action from the goal, but it remains highly readable and to the point.

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 complexity (backtesting with parameters like window_size and step), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., accuracy metrics, plots, or summaries), how results are formatted, or any limitations (e.g., computational cost). For a backtesting tool with multiple parameters and no structured output documentation, this leaves too much unspecified for effective agent 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 clear descriptions for all parameters (lottery_type, window_size, step). The description adds no additional meaning beyond the schema, such as explaining how parameters interact (e.g., window_size defines training data, step controls backtest increments) or providing usage examples. With high schema coverage, the baseline score of 3 is appropriate, as the schema adequately documents parameters without extra help from the 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?

The description '回测预测算法，评估预测准确性' (Backtest prediction algorithm, evaluate prediction accuracy) states a general purpose but lacks specificity about what resource it operates on. It mentions 'prediction algorithm' but doesn't clarify if this is about lottery predictions specifically or what exactly is being backtested. While it distinguishes from siblings like 'predict_lottery' by focusing on evaluation rather than prediction, it remains somewhat vague about the exact 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing historical data), exclusions, or comparisons to sibling tools like 'predict_lottery' (for making predictions) or 'get_historical_data' (for data retrieval). Without such context, an agent must infer usage from the tool name and parameters alone.

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