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

Because the annotations only supply openWorldHint and no read-only/destructive hints, the description bears responsibility for behavioral disclosure. It discloses that results are merged text for total/production/sales/dependency types, that it is limited to a two-year window, and that count-only output is not returned. This adds meaningful behavioral context beyond the structured annotations.

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: it states the purpose, lists included metrics, states exclusions, and provides query examples. It is reasonably front-loaded and scannable. However, it contains some redundancy in the indicator list and includes the pricing block, which is operational metadata rather than semantic guidance, preventing a full 5.

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?

The description provides enough context to select this tool: it defines the time window, the region/chain parameters, the list-based return, and the major exclusions. Given that an output schema exists and the input schema is fully described, the description need not detail return fields. It is adequate for a moderately simple list tool among many siblings.

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 provides 100% coverage with useful descriptions for region, chain_name, and year. The description reinforces these via examples and the geographic granularity, but it adds little beyond what the schema already tells the agent. Therefore, without an annotation for parameter semantics, only the schema-informed baseline can be stated.

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 core action: with a specific region and industry chain input, return the list of companies that issued tenders in the past two years. It also explicitly enumerates the sub-types it covers (total, production, sales, dependency) and what it excludes (count-only results and other classifications), which clearly distinguishes it from sibling count/list tools.

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 gives clear usage context through parameter and example-based typical questions (e.g., '2024年全国集成电路近两年发起过招标的企业名单'). It also includes '不包含' exclusions that help the agent avoid inappropriate uses. However, it does not explicitly name sibling alternatives such as chain_issued_tender_company_num or chain_participated_tender_company_list, so it falls one step short of fully explicit when-to-use vs. alternative guidance.

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