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

With annotations limited to openWorldHint, the description adds useful behavior context: it clarifies that the tool covers specific competitor dimensions (subsidies, channels, new products, tech, pricing) and explicitly excludes other indicator categories and batch list queries. This goes beyond the annotation without contradicting it. It does not disclose output shape, but the output schema exists to cover that.

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 clear one-line purpose, a list of covered indicators, an exclusion list, and representative questions. Slightly verbose but every section serves a functional role, and the typical-question examples are useful rather than redundant.

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 a clear annotation, a full input schema covering 100% of parameters, and an output schema, the description adds sufficient scope boundaries and exclusions that make the tool complete for selection and invocation. No critical context—such as own-enterprise unsuitability or batch list exclusion—is missing.

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 the input schema already explains both parameters (company_name and country_name) with examples. The description reinforces their use through example questions but does not add material semantic information beyond what the schema provides, so the 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's function: querying competitors' periodic market changes by enterprise name, specifically covering subsidy/penalty, channel, new product, technology, and pricing moves. It also explicitly distinguishes itself from tools that query the user's own enterprise or batch-listed companies, making its scope unmistakable.

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 explicit when-not guidance: not for own enterprise metrics, not for non-category indicators, and not for park/industry-chain batch filtering. It also gives typical example questions. However, it does not name specific alternative sibling tools to use instead, so it lacks complete alternative-based guidance.

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