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

Beyond the minimal annotation (openWorldHint), the description discloses key behavioral aspects: it returns a merged text of total/production/sales/dependent types, and it excludes other categories and count-only outputs. This sets clear expectations about the tool's output structure and limitations, though it does not mention pagination or error handling.

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-organized with clear sections: purpose, included metrics, exclusions, and example queries. It is a bit long due to the inclusion of pricing info and multiple examples, but each part contributes to understanding the tool's function. It is front-loaded with the core purpose and then details.

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 straightforward query interface (region + chain + optional year) and an output schema exists, the description covers the necessary context: what it returns, what it excludes, and example usage. It does not describe edge cases or empty results, but for a list tool with a clear output schema, this is adequate.

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 input schema already covers all three parameters with descriptions, and the description adds semantic context by explaining that region can be country/province/city/district and chain_name refers to an industry chain name. The typical questions also illustrate plausible values, enhancing the meaning of each parameter beyond the schema.

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: querying a list of companies with patents based on a specific region and industry chain name. It also lists the exact types of company lists returned (total, production, sales, dependency) and explicitly states what is not included. This distinguishes it from sibling tools like chain_have_patent_company_num (which returns counts) and chain_have_no_patent_company_list.

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 and clarifies scope via '不包含' statements, but it does not explicitly name alternatives or state when to use this tool over its num counterpart or other list tools. The examples and exclusions make the intended usage fairly clear, but explicit 'use when' guidance is absent.

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