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

Annotations provide openWorldHint: true, but the description does not contradict it. The description explains the return format (text combining totals and types) and excludes certain data, which adds context beyond the annotation. However, it does not disclose other behaviors like pagination or potential delays, and with only one annotation, the description could carry more weight. The absence of a contradiction allows a neutral score.

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, with about three sentences and a list of examples. It is front-loaded with the core purpose and includes a pricing object which is extraneous but not harmful. The structure is clear, but the pricing information could be omitted for better focus, though it is not a significant detraction.

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 that the tool has an output schema (has_output_schema: true) and the description references a merged text output, the description is largely sufficient. However, the tool is part of a large family of chain_* tools, and the boundary between 'new_start' and other indicators (e.g., 'year5' or 'a_taxpayer') could be clearer. The description does not explicitly state that this tool only counts new starts, but the name does. Overall, adequate but not exhaustive.

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?

The schema descriptions for the three parameters are clear and self-explanatory (e.g., 'region' as area name, 'chain_name' as industry chain name). The description adds value by emphasizing that the query is for 'new start' companies and the year is optional, but it does not provide additional semantic details beyond the schema's 100% coverage. Baseline 3 is appropriate as the schema does the heavy lifting.

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 purpose: to query the number of newly started companies for a specific region and industry chain, with a breakdown by type. It differentiates from siblings by emphasizing 'new start' and the region/chain context, and notes it excludes company lists and other classifications. However, it does not explicitly name a sibling, so it slightly misses the full distinctiveness criterion.

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 question examples to illustrate when to use the tool, such as '2024年全国集成电路当年新增的企业有多少'. It also indicates what is not included (other classifications, company lists), but does not explicitly say when not to use it or suggest alternatives, which would be a score of 5. The exclusions help the agent avoid misusing it.

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