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

With only openWorldHint annotation, the description carries the behavioral burden and adds meaningful context: the two-year time window, merged text return of four metric types, and exclusions. It does not mention read-only nature, but the query semantics and output schema cover the expected behavior adequately.

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 with a main sentence, metric breakdown, exclusions, and examples. The metric list slightly overlaps the first sentence but each section contributes useful disambiguation, so it remains efficient and scannable without being bloated.

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 covers purpose, parameter examples, return content, exclusions, and typical usage. An output schema exists, so return values need no further explanation. Minor gaps include the default behavior when 'year' is omitted and handling of zero-results, but these are not critical for a count query tool.

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 value beyond the schema by clarifying region granularity (country/province/city/district) and providing concrete examples for chain_name and year (e.g., 集成电路, 新能源, 2024), which reduces ambiguity for the agent.

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 counts companies with outward investment in the past two years, filtered by region and industry chain. It explicitly lists the returned metrics (total/production/sales/dependent) and excludes other classifications and company lists, distinguishing it from sibling chain_*_num and chain_invest_company_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 provides typical user questions and an explicit 'not included' section (other classifications, company lists), which helps the agent decide when this tool is appropriate. However, it does not directly name alternative tools like chain_invest_company_list for detail retrieval, relying on the exclusion statement to imply the distinction.

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