research_company

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

There are no annotations to rely on, so the description must disclose behavioral traits. It mentions 'using AI' but omits potential limitations such as scraping failures, website accessibility issues, time delays, or data quality variability. This is a significant gap for a web scraping tool.

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 a single sentence that is front-loaded with the action, resource, and output. Every word is necessary, with no redundancy or filler. It is highly concise and efficient.

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 there is no output schema, the description should provide enough detail about return values and edge cases. It lists the extracted data (mission, products, culture, tech stack, news) but does not specify how results are returned, handling of missing websites, or interaction between url and company_id. This makes it minimally viable but with clear gaps.

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 covers all parameters (company_id required, url optional) with clear descriptions, so the baseline is 3. The description adds no extra parameter-level semantics but does inform about the output fields, which indirectly clarifies intent. It neither strengthens nor weakens the schema's clarity.

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 a specific verb ('scrape') and resource ('a company's website') and enumerates the extracted data types. It is distinct from sibling tools like search_companies or generate_email, establishing a unique purpose.

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 implies usage when needing company information scraped from a website. It does not explicitly contrast with alternatives, but the sibling tool names and context make the intended use case clear. It lacks an explicit 'when not to use' but still provides adequate context.

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