个股资金面：获取股票主力资金流向、龙虎榜、两融数据

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 only lists data types without mentioning permissions, rate limits, data freshness, or output format. For a tool fetching financial data, this lack of behavioral context (e.g., real-time vs. historical, authentication needs) 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 phrase that repeats the title, making it concise but under-specified. It front-loads the purpose but lacks structure or elaboration that could add value. While not verbose, it fails to use its limited space effectively to enhance understanding beyond the title.

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 (financial data analysis with one parameter), the description is minimally complete. It identifies data types but lacks behavioral details, and with an output schema present, it doesn't need to explain return values. However, without annotations and with vague usage guidelines, it leaves gaps in contextual understanding 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 the parameter 'query' documented as 'A股股票名称或代码' (A-share stock name or code). The description adds no additional parameter semantics beyond this, such as format examples or constraints. Since the schema already provides adequate coverage, the baseline score of 3 is appropriate.

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?

Tautological: description restates name/title.

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?

No guidance is provided on when to use this tool versus alternatives. The description lists data types but doesn't specify use cases, prerequisites, or exclusions. With siblings like 'stockFunAnalysis' and 'sectorCapAnalysis' that might offer similar or complementary data, this omission leaves the agent without context for tool selection.

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