Skip to main content
Glama
jcnh74

linkedin-profile-manager-mcp

by jcnh74

Create LinkedIn post (draft-first)

create_linkedin_post

Draft LinkedIn posts with preview, then publish via official API after human approval using approval token.

Instructions

[risk: publishes-content | confirmation required] Can publish via the OFFICIAL LinkedIn API (OAuth, w_member_social). Requires confirm=true AND enableOfficialPosting in config. Draft a post. Publishing requires: preview call → human approval → confirm=true with approvalToken, AND official OAuth (LINKEDIN_ACCESS_TOKEN, w_member_social) AND enableOfficialPosting=true in config.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesPost body (LinkedIn cap: 3000 chars).
confirmNofalse = draft/preview only. true = publish (requires approvalToken from the preview step).
visibilityNoPUBLIC
approvalTokenNo
Behavior5/5

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

No annotations provided, so the description fully carries the burden. It explicitly lists risks (publishes-content, confirmation required), required OAuth scopes, the need for human approval, and the confirm flag with approvalToken. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is verbose and repetitive, restating the publishing requirements. It could be condensed into a more structured format, e.g., bullet points.

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?

Despite no output schema, the description covers the core workflow adequately. However, it does not specify what the tool returns (e.g., post ID or URL), leaving a gap for agents needing return values.

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 description coverage is 50%, and the description adds context for confirm and approvalToken in the workflow. However, it does not elaborate on the other parameters (text, visibility) beyond the schema, so baseline score is appropriate.

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 title and description clearly state the tool creates LinkedIn posts with a draft-first workflow. It distinguishes itself from siblings by being the only post-creation tool in the list.

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 explains the required configuration (enableOfficialPosting, OAuth) and the two-step process (preview then confirm). It does not explicitly exclude use cases, but as the sole posting tool, differentiation is not necessary.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jcnh74/linkedin-profile-manager-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server