get_company_count

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

With no annotations provided, the description fully carries the burden. It discloses the entitlement requirement, the error behavior ('no_entitlement'), authentication via env var or .env file, and notes that employee bounds are inclusive. This is far beyond typical transparency and gives an agent critical operational knowledge.

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-organized with a summary, bulleted use cases, 'Args:' block, and a prerequisites paragraph. Every section serves a purpose and the front-loaded summary immediately communicates what the tool does. It is slightly long but structurally efficient and scannable.

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, when to use, all parameter semantics, prerequisite conditions, authentication, and error handling. Since an output schema is present, not describing return values is acceptable. It provides a complete operational picture for an agent to select and invoke the tool without guessing.

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 schema has 0% description coverage, but the description compensates by explaining every parameter with examples: 'naics' with a semiconductor example, 'countries' with country lists, and 'employee_min/employee_max' with band bounds and the inclusive keyword. This fully resolves the otherwise undocumented parameters.

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 it 'Count companies in Cognism' with specific filtering dimensions (NAICS, countries, employee range). It distinguishes itself from siblings by explaining it 'Fills the cells Eurostat/Nomis can't' and explicitly notes Cognism uses NAICS rather than NACE, which clarifies its unique role among count 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 explicit use cases ('Corp Large 5,000+', 'Corp Mid-cap 250-4,999', 'UK corporates') and a crucial prerequisite ('Requires the Cognism API entitlement'). It does not explicitly name alternative sibling tools, but the concrete examples and NAICS/NACE mapping give clear context for when this tool is appropriate.

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