linkedin-mcp-server

linkedin_create_post

Publish LinkedIn text posts with visibility options: public, connections, or logged-in members. Supports 3000 characters and auto-generates hashtags.

Instructions

Create a text post on LinkedIn. Supports different visibility options: PUBLIC (everyone), CONNECTIONS (1st degree connections only), or LOGGED_IN (LinkedIn members only).

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": {
      "text": "<text>"
    },
    "name": "linkedin_create_post"
  }
}

Input Schema

Name	Required	Description
`text`	Yes	The content of the post (max 3000 characters)
`hashtags`	No	Optional hashtags. If omitted, hashtags are auto-generated and appended at the end of the post.
`visibility`	No	Post visibility: PUBLIC (default), CONNECTIONS, or LOGGED_IN

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 9, 2026

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the core action (create) and visibility options, but does not mention authentication requirements, side effects, or the irreversible nature of posting. It adds some context but lacks depth.

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 two sentences, front-loaded with the core purpose, and every word earns its place. No fluff or redundancy.

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?

Given the simplicity of the tool (3 parameters, no output schema), the description provides enough context for an agent to understand the tool's function and key options. It could mention posting behavior or response, but the schema covers parameters adequately.

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?

Schema coverage is 100%, so the baseline is 3. The description adds semantic value by explaining what each visibility value means (e.g., CONNECTIONS = 1st degree connections only), which goes beyond the schema's simple enum listing.

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 and resource: 'Create a text post on LinkedIn.' It distinguishes from sibling tools by explicitly saying 'text post,' which differentiates it from create_article_post and create_image_post.

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 specifies this is for text posts, implying it should be used for that purpose rather than article or image posts. However, it does not explicitly name alternatives or state when not to use, so it falls just short of a 5.

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

Deploy Server