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

Annotations already declare the tool as read-only and non-destructive, so the description doesn't need to repeat that. It adds value by disclosing the specific outputs (funding history, leadership, etc.), which are not captured in the schema or annotations. It could go further by mentioning data freshness or coverage limits, but the provided content is sufficient for a research 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, well-structured sentence with a front-loaded purpose and a clearly enumerated list of research areas. Every word earns its place, and there is no wasted text.

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 no output schema, the description does a good job of listing what the tool returns. It also benefits from read-only annotations. However, it does not explain the website parameter, and there is no mention of data sources or limitations, leaving some gaps in full contextual completeness.

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?

With zero schema description coverage, the description was expected to explain the purpose of the two parameters (company_name and website). It does not: the description only mentions 'company' without clarifying that company_name is required for identification and website is optional for disambiguation. This is a significant gap.

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 identifies the tool's function as performing deep research on a company and lists specific data areas (funding history, leadership, tech stack, news, headcount, growth signals). This distinguishes it from sibling tools like search_companies or find_competitors, which are more general or narrowly scoped.

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 phrase 'Deep research' implies a use case for comprehensive company intelligence, providing clear context for when to use the tool. However, it does not explicitly mention alternatives or state when not to use it, so it stops short of full guidance.

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