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

With no annotations, the description carries the burden and does well: it discloses the scoring methodology, equal weighting, 0-100 per dimension, fast response ('秒回'), no assumption parameters, relative vs intrinsic scope, and API key requirements/rate limits. It stops short of describing the exact return shape or failure modes.

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 well-structured and front-loaded: a one-line summary of the composite score, a concise bullet list of dimensions with sub-metrics, then caveats and routing. Every sentence adds value, and the length is justified by the tool's methodology.

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?

The description is largely complete for a tool with no annotations and no output schema: it covers purpose, methodology, alternatives, key requirements, and ticker-format reference. The main gaps are the lack of explicit guidance about get_relative_valuation and no detail on the exact output structure, though the scoring description implies the return content.

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 0%, but the description compensates by adding conditional meaning: ticker format is said to match run_dcf, and fmp_api_key is conditionally required depending on market (A股/港股 no key, 美股/日股 key or limited free quota). This goes beyond the bare schema, though ticker format itself is delegated to a sibling rather than fully specified.

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 computes a four-dimensional composite score (估值/质量/成长/动量, equal 25% weights) as a quick company health check. It distinguishes itself from run_dcf by explicitly saying it is a relative-indicator check, not an intrinsic-value judgment, but it does not explicitly differentiate from the sibling get_relative_valuation.

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 gives explicit when-to-use guidance: use it for '这公司整体怎么样' and use run_dcf for '值多少钱', and it states the key prerequisites for different markets (A股/港股 no key; 美股/日股 need FMP key or free quota). However, it never mentions when to prefer get_relative_valuation instead.

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