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

Annotations only include openWorldHint: true, which is generic. The description adds valuable behavioral context: it returns merged text of totals and type-specific lists, includes specific categories (production, sales, dependent), and excludes other enterprise classifications. It also clarifies it returns lists, not just counts. This goes beyond the annotation and helps the agent understand output structure.

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-structured: first sentence states the core function, then lists the four indicator types, then what's excluded, then typical query examples. It's concise and front-loaded with the purpose. Every sentence adds value.

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 (3 parameters, output schema exists), the description is quite complete: it defines the time window, region/chain inputs, output types, exclusions, and examples. The output schema exists, so return values are covered. The main gap is not specifying the exact format of the merged text or whether it returns just names or details, but examples cover typical usage.

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%: all three parameters (region, chain_name, year) are described in the schema. The description doesn't add much beyond the schema for parameters, but it does clarify that region can be like '全国' or '成都市', which matches the schema. The description mentions '近两年' and '2024' examples, tying year to the time window, but that's implicit. Baseline 3 is appropriate given high schema coverage.

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 external financing in the past two years based on specific region and industry chain. It distinguishes from siblings like chain_invest_company_list (companies that invested) and chain_invested_company_num (count only) by specifying it returns lists and merges totals/type-specific texts. The name and title align perfectly.

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 usage context: it's for querying companies with external financing in the past two years, and explicitly lists what is not included. It gives typical query examples. However, it doesn't explicitly state when to use this vs. alternatives like chain_invested_company_num or other list tools, but the clear differentiation via '近两年有对外融资' and '列表' plus sibling names makes it reasonably clear.

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