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

The description discloses that the tool merges and returns aggregate text for total/production/sales/dependent counts, and explicitly states it excludes company name lists. This adds useful behavioral detail beyond the openWorldHint annotation, with no contradiction (the annotation remains compatible).

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 logically structured: purpose, returned metrics, exclusions, and examples. It is mostly concise, but the pricing block and repetitive metric list add slight noise that prevents a 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?

Holds the 3-parameter input schema is fully described. The description covers what the tool does, what metrics are returned, what is excluded, and gives clear example queries. This is complete enough for an agent to select and invoke the tool effectively.

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%, so the baseline is 3. The description adds useful parameter context by specifying granularity levels (country, province, city, district) and provides realistic region+chain_name+year example queries that clarify how the parameters should be combined.

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 counts of tech-oriented SMEs by region and industry chain, with a specific verb and resource. It meaningfully distinguishes from siblings by explicitly excluding enterprise list details and other enterprise classifications, matching its list-vs-count sibling relationship.

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?

Provides clear context: county/city/province/country regions, specific chain names, and typical example queries. It does not explicitly name alternative tools like chain_tech_oriented_company_list, but the exclusions ('不包含') clarify the boundary of what this tool covers.

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