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devag7

LinkedIn MCP

get_company_employees

Retrieve a list of employees for a company using its LinkedIn URL slug. Returns names, headlines, and identifiers for prospecting.

Instructions

List employees LinkedIn surfaces for a company (by URL slug). Returns name, headline, and public identifier (feed the slug to get_profile). Prospecting core.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
universal_nameYesCompany URL slug, e.g. "anthropicresearch"
countNoEmployees to return (default 10)
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns name, headline, and public identifier, but does not mention safety (read-only), rate limits, authentication needs, or that the list may be limited to what LinkedIn surfaces. The behavioral transparency is adequate but not thorough.

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

Conciseness5/5

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

The description is two sentences with no superfluous words. It front-loads the action ('List employees'), specifies the input method, and summarizes return fields and use case. Every sentence adds value.

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

Completeness4/5

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

For a simple list tool with no output schema, the description tells the agent what fields to expect (name, headline, identifier). It does not explain count parameter behavior (e.g., pagination) or error handling, but the schema already provides the count default and limits. The description is largely complete for its purpose.

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

Parameters3/5

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

The schema covers both parameters with descriptions (universal_name: URL slug; count: employees to return, default 10). The description adds minor context by noting the slug can be fed to get_profile, but does not significantly enhance understanding beyond the schema. Baseline of 3 for 100% coverage is appropriate.

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 clearly states the tool lists employees for a company, specifies the input as a URL slug, and details the returned fields (name, headline, public identifier). It also provides a usage hint for the next step (get_profile). This distinguishes it from sibling tools like get_company or get_profile.

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

Usage Guidelines3/5

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

The description implies usage for prospecting and hints at a workflow (feed slug to get_profile), but it does not explicitly state when to use this tool versus alternatives like search_people or when not to use it. No exclusion criteria or alternatives are mentioned.

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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