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LinkedIn MCP Server

LinkedIn MCP Server

by aarnav-hariramani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_person_profile

Retrieve a person's LinkedIn profile by username. Optionally include extra sections like experience, education, and posts for deeper data.

Instructions

Get a specific person's LinkedIn profile.

Args: linkedin_username: LinkedIn username (e.g., 'satyanadella', 'jeffweiner08') sections: Comma-separated list of extra sections to scrape. The main profile page is always included. Available sections: experience, education, interests, honors, languages, contact_info, posts Default (None) scrapes only the main profile page.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "linkedin_username": "<linkedin_username>"
    },
    "name": "get_person_profile"
  }
}

Input Schema

NameRequiredDescriptionDefault
sectionsNo
linkedin_usernameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
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 describes that the main profile is always included and sections are optional, but does not disclose potential behaviors like error handling, rate limits, or data volatility. This is adequate but not comprehensive.

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 clear and front-loaded with the purpose, but the Args section is slightly verbose. Each sentence adds value, but could be slightly more concise without losing information.

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?

The description covers parameters well and the tool has an output schema, so return values are not needed. It lacks behavioral or error context, but for a simple data retrieval tool, it is reasonably complete. Minor gaps remain.

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 coverage is 0%, meaning the schema itself provides no descriptions. The description adds significant meaning: examples for linkedin_username, a list of available sections with their behavior, and default behavior. This fully compensates for the schema gap.

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 explicitly states the tool retrieves a specific person's LinkedIn profile, using a clear verb+resource structure. It distinguishes itself from siblings like 'search_people' and 'get_company_profile' by focusing on an individual profile.

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

Usage Guidelines4/5

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

The description provides parameter usage details (linkedin_username format, sections options) but does not explicitly state when to use or avoid this tool relative to alternatives. It implies use for a specific person, which is clear, but lacks direct guidance.

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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