LinkedIn MCP

linkedin_draft_profile_changes

Drafts suggested headline, About, and experience text based on current profile, target role, skills, and achievements, without editing LinkedIn; use for review before applying.

Instructions

Prepare suggested headline/About/experience text. This tool drafts only; it does not edit LinkedIn.

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": {
      "current_profile": "<current_profile>",
      "target_role": "<target_role>",
      "skills": "<skills>"
    },
    "name": "linkedin_draft_profile_changes"
  }
}

Input Schema

Name	Required	Description	Default
`skills`	Yes
`target_role`	Yes
`achievements`	No
`current_profile`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 29, 2026

TDQS

B3.4/5.0

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 it does disclose the critical behavioral trait that this is a non-mutating draft-only operation with no effect on the live LinkedIn profile. It omits permission/auth requirements and what the returned draft looks like, but the most important side-effect question is answered.

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?

Two tight sentences with no wasted words, and 'drafts only; does not edit' is front-loaded. Efficient, though arguably under-specified rather than truly concise about parameters.

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-value explanation isn't needed, and the draft-only semantics are covered. However, with 4 undocumented parameters at 0% schema coverage and no usage workflow, the definition leaves real gaps for correct invocation.

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 description coverage is 0% and the description says nothing about the four parameters (current_profile, target_role, skills, achievements). An agent has no guidance on format, whether 'skills' is comma-separated, or what 'achievements' should contain, so the description fails to compensate for the coverage gap.

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 uses a specific verb ('Prepare suggested') and names the concrete resources it drafts: headline, About, and experience text. It clearly signals a drafting operation distinct from editing, giving an agent enough to distinguish it from publish/edit siblings, though it doesn't name those siblings explicitly.

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?

It implies usage (draft profile content) and states a key exclusion ('does not edit LinkedIn'), but it doesn't name the alternative that actually publishes or apply changes, nor does it explain the workflow relationship to siblings like linkedin_publish_approved_post. Usage is implied rather than prescribed.

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

Deploy Server