Get Company Employees

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

With annotations readOnlyHint=true and openWorldHint=true, the description adds valuable context beyond them: the demographics expose locations, education, and function breakdowns; the keywords filter behavior; and the critical caveat that company_name must be the exact URL slug, not the display name (with Anthropic example). This enriches the agent's understanding of tool behavior.

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 and front-loaded: first the purpose, then usage alternatives, then parameter specifics. Every sentence carries essential information, with no fluff. The slug example is concise but effective. It is appropriately sized for the tool's complexity.

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 the presence of an output schema and annotations, the description is complete enough. It covers purpose, when to use alternatives, parameter semantics, and a critical edge case (slug mismatch). Nothing essential is missing for the agent to select and invoke the tool correctly.

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 baseline is 3. The description adds value by explaining that company_name must be the exact LinkedIn URL slug with a concrete example ('anthropicresearch' not 'anthropic'), and clarifies that keywords filters by name/title/skill. This goes beyond the schema's simple field descriptions.

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 states a specific action ('List employees at a company') and a specific resource (the LinkedIn /people/ page), and further distinguishes itself from siblings by noting the demographics aggregate is 'unique to this tool.' This clearly differentiates it from search_people and other sibling 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?

Explicit guidance is provided: 'For filtered search by network degree (1st/2nd/3rd) or location, prefer search_people with current_company set to the company URN id.' It also recommends calling search_companies when the slug is uncertain. This gives clear when-to-use and alternatives.

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