linkedin-mcp

Publish a post

linkedin_publish_post

Publish a text post to a LinkedIn member's feed only after a quality gate validates the draft; runs as a dry run until publishing is enabled, so check drafts first and fix issues before going live.

Instructions

Publish a text post to the member's feed. The quality gate runs first and refuses to publish a draft that fails it. This is a dry run unless publishing has been enabled on the server. Prefer linkedin_check_post first, so you can fix the problems it finds.

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_publish_post"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes
`dry_run`	No
`visibility`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 23, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does real work: it discloses a pre-publish quality gate that can refuse the draft, that execution is a dry run unless server-side publishing is enabled, and that the tool is a mutation. It leaves the interplay between the dry_run parameter and the server setting, plus auth requirements, unstated.

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?

Four short sentences, front-loaded with the action, then the gate behavior, then the routing advice. Nothing is filler, though the dry-run sentence is worded loosely enough to read as ambiguous rather than crisp.

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?

An output schema exists, so return values need not be explained. But for a three-parameter mutation with zero schema description coverage and no annotations, the definition omits the visibility semantics and text limits and does not clarify dry_run precedence, leaving real gaps before an agent can call it confidently.

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

Parameters2/5

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

Schema coverage is 0%, so the description must compensate and largely does not. It never explains the text parameter's constraints, how the dry_run parameter relates to the server toggle described in prose, or what values the visibility parameter (default empty string) accepts.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: "Publish a text post to the member's feed." The quality-gate sentence hints at how it differs from linkedin_check_post, but the differentiation is carried mainly by the guidance sentence rather than the purpose statement itself.

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?

"Prefer linkedin_check_post first, so you can fix the problems it finds" explicitly names the sibling and the ordering to follow. It stops short of saying when not to use it (e.g. if the token is invalid, returning to linkedin_token_status).

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

Deploy Server