MCP Server for LinkedIn

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Get Company Posts

get_company_posts

Read-only

Retrieve recent posts from any company's LinkedIn feed by entering the company name. Monitor their updates, announcements, and shared content.

Instructions

Get recent posts from a company's LinkedIn feed.

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

Name	Required	Description	Default
`company_name`	Yes	LinkedIn company name (e.g., "docker", "anthropic", "microsoft")

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv4.20.1Aug 6, 2026

TDQS

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds no extra context about authentication, rate limits, or side effects. It does not contradict the annotations, but it also does not provide additional behavioral details beyond what the annotations already imply.

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 a single, concise sentence with no unnecessary words or repetition. It is well-structured and directly states the tool's function.

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

Completeness5/5

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

The tool is simple and the description sufficiently covers its purpose. Since an output schema exists (per context signals), the description does not need to explain return values, and no additional context is required for this straightforward operation.

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 provides a description for the only parameter (company_name) with examples. The tool description does not add further semantic meaning beyond the schema, and since the schema coverage is high, the baseline of 3 applies.

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 action ('get') and the resource ('recent posts from a company's LinkedIn feed'). It effectively differentiates from sibling tools like 'get_company_profile' or 'get_company_employees' by specifying the data type (posts).

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 does not explicitly state when to use this tool versus alternatives, nor does it provide conditions or exclusions. However, the purpose is so straightforward that the usage context is implicitly clear for someone familiar with LinkedIn tools.

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