Get Company Employees

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate safety. It adds meaningful behavioral context: the data comes from the /people/ page, demographics are unique to this view, and the company_name must be the exact URL slug, not the display name, with a concrete example. This goes beyond annotations without contradicting them.

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 moderately long but every sentence earns its place: core purpose first, then usage routing, then keyword semantics, then the critical slug caveat. The Anthropic example is concrete and high-value, not filler. Structure is logical and front-loaded.

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 output schema exists, annotations cover safety, and the description covers purpose, routing, param semantics, and a caveat with an example, the definition is complete for an agent to select and invoke the tool correctly. No critical gap is evident.

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%, but the description adds substantial semantic value beyond the schema. It clarifies that company_name must be the exact LinkedIn URL slug, warns that display names often differ, gives the Anthropic example, and tells the agent to resolve ambiguity via search_companies. This is exactly the kind of parameter nuance that prevents incorrect calls.

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 'List employees at a company from the LinkedIn /people/ page' and uniquely identifies its value-add: the demographics aggregate (location, education, function breakdown). It distinguishes itself from siblings by noting these demographics are unique to this tool, so an agent can immediately tell it apart from search_people or get_company_profile.

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 when to use this tool vs alternatives: for filtered search by network degree or location, prefer search_people with current_company set. It even notes search_people returns more result pages, and instructs the agent to call search_companies first if the slug is uncertain. This is strong, actionable routing guidance.

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