index_data

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 retrieves data but doesn't describe what kind of data (e.g., historical prices, volumes), the format of the response, potential rate limits, authentication needs, or error conditions. For a data retrieval tool with zero annotation coverage, 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 a single, efficient sentence that directly states the tool's purpose with relevant examples. It's front-loaded with the core functionality and wastes no words. Every part of the sentence earns its place by clarifying the scope.

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 of financial data retrieval, no annotations, and no output schema, the description is incomplete. It doesn't explain what data is returned (e.g., OHLC prices, volumes), the response format, or any behavioral aspects like pagination or errors. For a tool with three parameters and no structured output documentation, more context is needed.

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%, so the schema already documents all three parameters (code, start_date, end_date) with clear descriptions and examples. The description adds no additional parameter semantics beyond what's in the schema, such as date range constraints or code validation rules. 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: '获取指定股票指数的数据' (get data for specified stock indices). It specifies the resource (stock indices) and provides examples (上证指数, 深证成指). However, it doesn't explicitly distinguish this tool from sibling tools like 'stock_data' or 'fund_data', which might handle similar financial data but for different asset classes.

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 sibling tools like 'stock_data' (for individual stocks) or 'fund_data' (for funds), nor does it specify prerequisites or exclusions. The agent must infer usage from the tool name and description alone.

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