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LinkedIn MCP Server

LinkedIn MCP Server

by aarnav-hariramani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_company_posts

Fetch recent posts from a company's LinkedIn feed by providing the company name.

Instructions

Get recent posts from a company's LinkedIn feed.

Args: company_name: LinkedIn company name (e.g., 'google', 'stripe', 'openai')

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": {
      "company_name": "<company_name>"
    },
    "name": "get_company_posts"
  }
}

Input Schema

NameRequiredDescriptionDefault
company_nameYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description bears full responsibility. It fails to disclose behavioral traits such as authentication requirements, result limits, pagination, or rate limits. The description is too minimal for a read operation.

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. However, it could include more detail without becoming verbose, given the simplicity of the tool.

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 an output schema present, the description does not need to explain return values. However, it lacks context about prerequisites (e.g., logged-in session) and usage scope, which are relevant given sibling tools like start_login.

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?

The description adds meaning beyond the schema by providing example values (e.g., 'google', 'stripe', 'openai') for the company_name parameter, which has 0% schema description coverage. However, it does not explain the exact format or validity rules.

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 verb 'get' and the resource 'recent posts from a company's LinkedIn feed'. It provides examples of company names, distinguishing it from sibling tools like get_company_profile or get_job_details.

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?

No guidance on when to use this tool vs alternatives, no prerequisites or exclusions. Sibling tools are listed but not compared, leaving the agent to infer usage context.

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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