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

Beyond the minimal openWorldHint annotation, the description discloses the scope of data covered (products, competitors, market share, certifications, recalls, etc.) and exclusions (non-category indicators, park/industry-chain list screening). It effectively communicates the tool's query-oriented behavior, though it does not describe output mechanics or limits beyond what the output schema presumably provides.

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 front-loaded with the core purpose, then organized into inclusion metrics, exclusions, and typical questions. It is longer than minimal due to the detailed metric list, but the structure makes it scannable and the content is mostly non-redundant with the schema and annotations.

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 large sibling set and the tool's domain complexity, the description is comprehensive: it states the exact query subject, lists included indicators, explicitly outlines exclusions, and provides representative questions. The presence of an output schema and full parameter schema coverage further reduces missing context, making this description well-rounded for an AI 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 already have clear descriptions with examples. The description adds typical question examples that illustrate how to use company_name and country_name together, but this is marginal value beyond the schema rather than substantive new semantic detail.

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 queries enterprise-level periodic changes in business operations related to product layout, market performance, supply risk, and technology impact. It distinguishes itself from sibling tools by explicitly saying it is not for product-name market-wide searches and not a substitute for dedicated competitor-move queries.

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-to-use guidance (not for product-name searches, not for dedicated competitor analysis) and includes typical question formats for when it should be used. However, it does not name specific alternative sibling tools, so the guidance is clear but slightly less actionable than it could be.

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