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linkedin-mcp

Check a post before publishing it

linkedin_check_post

Run a quality gate on a LinkedIn draft to block errors like announcement formulas, emoji punctuation, greeting first lines, missing links, and empty-length posts before publishing. Warnings do not block.

Instructions

Run the quality gate over a draft post and return what is wrong with it. Errors block publishing: the announcement formulas, emoji used as punctuation, a first line that is a greeting rather than a claim, a post with no link in it, and a length that says nothing. Warnings do not block. Use this before every post.

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden well: it explains that errors block publishing, warnings do not, and it lists the main error categories. It still does not state whether the tool has side effects or auth requirements, but the core behavior is clear.

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 front-loaded with the purpose and then enumerates error conditions concisely. The list is useful, though slightly longer than strictly necessary and repeats the pre-publish framing from the title.

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?

An output schema exists, so the description need not explain return values. It adequately covers the checking behavior and error/warning distinction, though it leaves some gaps around side effects and explicit parameter definition.

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?

There is one required string parameter and schema description coverage is 0%. The description implies the parameter is the draft post text, but it does not explicitly define the parameter or its format, so it only partially compensates for the missing schema documentation.

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?

The description states a specific verb and resource: it runs a quality gate over a draft post and reports problems. It is clearly a pre-publish validation tool, though it never names the sibling publish tool explicitly.

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?

It gives direct usage guidance: 'Use this before every post.' That tells the agent when to call it, but it does not explicitly describe when not to use it or name alternatives like linkedin_publish_post.

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