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

Annotations are minimal (only openWorldHint=true), so the description carries the full burden of disclosing behavior. It mentions that the result is a merged text (list of types) and excludes other categories, providing some transparency about output structure. It does not explicitly state whether the operation is read-only, has side effects, or requires permissions, but given the nature of a query tool and the open world hint, the described behavior is consistent and adequate though not rich.

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 states the core purpose first, lists included types, then exclusions, and ends with typical queries. The inclusion of pricing info is extra but not verbose. It front-loads the key information and avoids redundancy. Each sentence adds value, and the examples are helpful for quick comprehension.

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 only 3 parameters, an output schema (as indicated by context), and is a straightforward list query, the description is sufficiently complete. It explains what is returned (merged text of lists), what is excluded, and provides concrete examples. It does not cover pagination or ordering, but for this tool that is likely not critical. The description adequately prepares the user to invoke the tool correctly.

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% with descriptive text for each parameter (year, region, chain_name). The description adds value by providing typical query phrasing that demonstrates parameter usage (e.g., '成都市新能源产业链当年新增的企业列表'), which clarifies how to combine region and chain_name. While the schema already documents parameters, the examples and the statement about merging types (total/production/sales/dependent) enhance understanding of expected inputs.

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 a list of newly added enterprises for a specific region (country/province/city/district) and industry chain name, returning merged text for total/production/sales/dependent types. It distinguishes from sibling count tools (like chain_new_start_company_num) by explicitly noting it returns lists, not just counts. The typical query examples reinforce the precise action.

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 typical usage examples (e.g., '2024年全国集成电路当年新增的企业名单') that illustrate how to combine region, chain_name, and year. It also explicitly states what is not included (other categories, count-only returns), which implies when to use this tool vs alternatives. However, it does not explicitly name alternative tools (like chain_new_start_company_num) or provide 'when-not' guidance beyond these exclusions, so it is clear but not fully explicit.

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