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

Annotations only include openWorldHint:true, indicating the tool may not return exhaustive results. The description does not add behavioral details beyond listing included/excluded metrics and providing query examples. It does not contradict annotations, but with minimal annotation coverage, the description could provide more transparency on response behavior (e.g., data granularity, time range, or rate limits).

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 concise and well-structured. It opens with the core purpose, lists included metrics, exclusions, and typicall queries. Each sentence earns its place, and the pricing info is separate, not cluttering the main description. There is no redundancy or fluff.

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 moderate complexity (2 parameters, output schema exists) and the rich description covering purpose, exclusions, and examples, the description is largely complete. The output schema handles return value documentation, so the description doesn't need to explain output. Minor gap: it doesn't specify the time period for 'periodic changes' or data coverage, but with good schema and examples, it's adequately complete for agents.

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%, as both 'company_name' and 'country_name' have descriptions in the input schema. The tool description adds context on what these parameters are used for (querying by company and country) and provides example values (e.g., '比亚迪股份有限公司', 'Tesla, Inc.', '中国', '美国'). However, it does not add semantic depth beyond the schema descriptions, so a 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 queries periodic changes in employer brand aspects for a specific company, specifically returning average salary, benefits, and vacation days. It explicitly lists what is included and excluded, and provides example queries. This distinguishes it from sibling tools that focus on other enterprise change dimensions.

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 usage context: it is for querying employee salary, benefits, and vacation days by company name and country. It explicitly states what it is NOT for (recruitment dynamics, labor contracts, overtime casualties) and what it excludes (non-category indicators, batch screening by park/industry chain). This gives clear when-to-use and when-not-to-use guidance, with representative query examples.

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