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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Get Company Profile

get_company_profile
Read-only

Retrieve a company's LinkedIn profile using your authenticated browser session. Get the about page, and optionally include posts and jobs sections.

Instructions

Get a specific company's LinkedIn profile.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionsNoComma-separated list of extra sections to scrape. The about page is always included. Available sections: posts, jobs Examples: "posts", "posts,jobs" Default (None) scrapes only the about page.
company_nameYesLinkedIn company name (e.g., "docker", "anthropic", "microsoft")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is known. The description adds no additional behavioral context such as rate limits, data freshness, or how the sections parameter affects behavior, offering no value beyond the annotations.

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 a single short sentence, making it highly concise and front-loaded. However, it is too terse to capture the full scope of the tool (e.g., sections), so it earns a high but not perfect score.

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

Completeness3/5

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

With annotations, output schema, and complete parameter schema, the basic operation is clear. However, the description omits the fact that it can optionally scrape posts and jobs via the sections parameter, which could lead to confusion with get_company_posts. Thus it is adequate but not fully complete.

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?

All parameter descriptions are provided in the input schema, including detailed explanations for the optional sections parameter. The description itself does not touch on parameters, but since schema coverage is 100%, the baseline of 3 applies without needing to compensate.

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

Purpose4/5

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

The description uses a specific verb 'Get' with a resource 'a specific company's LinkedIn profile', clearly indicating the primary function. However, it does not distinguish from sibling tools like get_company_posts or search_companies beyond the name, so it is clear but not explicitly differentiated.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like get_company_posts or search_companies. There is no mention of scenarios or exclusions, leaving the agent to infer usage solely from the name.

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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