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Get Company Employees

get_company_employees
Read-only

Lists employees at a company from LinkedIn, with demographic aggregates like location, education, and job function. Use for unfiltered employee overviews; for filtered searches, use search_people with company URN.

Instructions

List employees at a company from the LinkedIn /people/ page, including the demographics aggregate that this view exposes: where employees live, where they studied, and a function breakdown (Engineering, Sales, Operations, etc.). The demographics are unique to this tool.

For filtered search by network degree (1st/2nd/3rd) or location, prefer search_people with current_company set to the company URN id. That path also returns more result pages than the /people/ tab.

The optional keywords filter narrows results by name, title, or skill.

company_name must be the exact LinkedIn URL slug (the path segment after /company/), not the display name. LinkedIn assigns unique slugs and the display name often does not match. For example, the AI lab Anthropic lives at /company/anthropicresearch/, not /company/anthropic/. If you are unsure of the slug, call search_companies first and pick the slug from the returned references.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNoOptional filter by name, job title, or skill (e.g., "engineer", "sales")
company_nameYesLinkedIn company URL slug (e.g., "docker", "anthropicresearch", "microsoft")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, but the description adds behavioral context: it derives data from a specific LinkedIn page, exposes unique demographics, and warns about fewer result pages compared to search_people. It also clarifies the slug requirement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is multi-paragraph and detailed, but every sentence adds value—use cases, alternatives, parameter constraints, and an example. While slightly longer than strictly necessary, it's efficiently structured around purpose, usage, and uniqueness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given annotations (readOnlyHint, openWorldHint), the description covers all necessary behavioral context: output includes aggregates, page limits relative to search_people, and the surprising slug mismatch. With an output schema and both params documented, no critical gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema describes both parameters with examplesags: company_name is a slug, keywords is optional filter. The description elaborates on the slug format and gives an example, but doesn't fully detail keyword matching semantics beyond 'name, title, or skill.' Schema coverage is 100% and description reinforces it, so a high score is warranted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource:

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly directs users to search_people for filtered searches by degree/location and notes the /people/ tab limitation, providing clear exclusions.

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

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