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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral context: 'Most recent reports first' explains ordering, and 'Source: Eastmoney datacenter (keyless)' clarifies data provenance and access requirements. No contradictions with annotations.

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 moderately concise at a few sentences, front-loading the main purpose and key details. It includes example queries and source information without being verbose. Could be slightly tighter, but still effective.

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 there is no output schema, the description comprehensively explains what the tool returns (profit range, YoY change, forecast type, reason) and includes ordering and data source. It also provides example symbol queries. For a simple 2-parameter tool, this is complete and sufficient for an agent.

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 coverage is 100% and both parameters have descriptions. The description does not add further parameter semantics beyond the schema, but it does explain the return value structure (e.g., profit range, YoY change), which indirectly helps parameter understanding. Thus, baseline 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?

The description clearly states the tool provides company-issued earnings guidance for Chinese A-share stocks, specifying the data points (profit range, YoY change, forecast type, reason). It distinguishes from sibling tools like ashares_analyst_consensus by emphasizing 'company's own forecast announcements' and includes example query terms, making the purpose unambiguous.

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 does not explicitly say 'use this tool when you need X, not Y', but it provides clear context: it answers queries about '业绩预告' and 'earnings forecast/guidance for a China A-share company'. The mention of source and default ordering implies appropriate use cases, but lacks explicit alternatives or when-not-to-use guidance.

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