Skip to main content
Glama
Dev-Anandhan

LinkedIn MCP Server

by Dev-Anandhan

get_company_profile

Retrieve a LinkedIn company's profile by name, including the about page, and optionally scrape posts and jobs sections.

Instructions

Get a specific company's LinkedIn profile.

Args: company_name: LinkedIn company name (e.g., 'google', 'stripe', 'openai') sections: Comma-separated list of extra sections to scrape. The about page is always included. Available sections: posts, jobs Default (None) scrapes only the about page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionsNo
company_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions that the about page is always included and sections are extra, but does not discuss side effects, authorization needs, rate limits, or data mutability. The behavioral disclosure is minimal.

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 concise and front-loaded with the purpose. The Args section is structured but could be slightly more compact. Overall, it efficiently conveys the key information without unnecessary words.

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?

Given the tool has an output schema (not shown but present), the description need not explain return values. However, it lacks usage guidelines and behavioral transparency. For a two-parameter tool, it covers input adequately but leaves gaps in completeness regarding when to use and behavioral context.

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

Parameters5/5

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

Schema description coverage is 0%, yet the description adds clear meaning: company_name is illustrated with examples ('google', 'stripe'), and sections are explained with its possible values ('posts, jobs') and default behavior. This adds significant value beyond the raw schema.

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's purpose: 'Get a specific company's LinkedIn profile.' It uses a specific verb ('Get') and resource ('company's LinkedIn profile'), which distinguishes it from siblings like get_job_details or get_person_profile.

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 parameter details but lacks explicit guidance on when to use this tool versus alternatives like get_company_posts. No context on prerequisites, limitations, or when-not-to-use is given.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Dev-Anandhan/LinkedIn-MCP-server-Dev-Anandhan_-'

If you have feedback or need assistance with the MCP directory API, please join our Discord server