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@isteam/linkedin-mcp

@isteam/linkedin-mcp

by isteamhq
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

create_post

Create a LinkedIn text post with up to 3000 characters. Publish updates, announcements, or thoughts directly to your LinkedIn feed.

Instructions

Create a LinkedIn text post (max 3000 chars)

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": "create_post"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesPost text (max 3000 chars)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It only discloses the max character limit (already in schema) and implies creation is destructive. It omits critical details like authentication requirements, idempotency, rate limits, or success/failure behavior. This is insufficient for a mutation tool.

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, well-structured sentence that conveys the core purpose and key constraint (max 3000 chars) without superfluous words.

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?

Given the tool has only one parameter and no output schema, the description is somewhat complete but lacks expected details for a creation tool, such as what the response contains (e.g., post ID) or confirmation of success.

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?

Schema coverage is 100% for the single parameter, so baseline is 3. The description adds no additional semantic information beyond what the schema provides (the parameter name 'text' and its constraints).

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 tool creates a LinkedIn text post, specifying the resource (text post) and action (create). It distinguishes from the sibling tool 'create_article_post', which handles article posts, making the purpose unambiguous.

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 implies the tool is for text-only posts, and the sibling name 'create_article_post' provides contrast. However, it lacks explicit guidance on when not to use this tool (e.g., for posts with media or links) or mention of alternatives.

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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