get_stock_analysis

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

No annotations are provided, so the description carries the full burden. It mentions the tool provides '数据驱动的分析报告' (data-driven analysis reports) and not investment advice, which hints at read-only behavior, but fails to disclose critical traits like whether it requires authentication, rate limits, data freshness, or error handling. For a tool with no annotations, 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 appropriately sized and front-loaded, starting with the core purpose, followed by Args and Returns sections. Every sentence adds value: the first sets scope, Args detail parameters, and Returns outline output. It could be slightly more concise by integrating the disclaimer into the purpose statement, but overall it's efficient.

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 has an output schema (Returns section describes the report content), the description doesn't need to detail return values. However, with no annotations and moderate complexity (analysis tool with parameters), it lacks completeness in behavioral aspects like permissions or limitations. The parameter semantics are covered, but overall context is adequate yet not fully comprehensive.

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?

The description adds meaningful context beyond the input schema, which has 0% schema description coverage. It explains that 'code' is a stock code like 'sh.600000' and 'analysis_type' has options ('fundamental', 'technical', 'comprehensive'), clarifying usage that the schema alone does not provide. With only 2 parameters, this compensates well for the low schema coverage.

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 '提供基于数据的股票分析报告' (provides data-driven stock analysis reports) with a specific verb ('提供') and resource ('股票分析报告'), distinguishing it from siblings that focus on raw data (e.g., get_balance_data, get_historical_k_data). However, it doesn't explicitly differentiate from get_hk_stock_analysis, which appears to be a Hong Kong-specific variant, leaving some ambiguity.

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 implies usage by specifying it provides '基于数据的股票分析报告，而非投资建议' (data-driven analysis reports, not investment advice), which suggests it's for informational analysis rather than decision-making. However, it lacks explicit guidance on when to use this tool versus alternatives like get_stock_basic_info for basic data or get_hk_stock_analysis for Hong Kong stocks, and no exclusions or prerequisites are mentioned.

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