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LinkedIn MCP Server (Salesbot)

by cz.salesbot

publish_linkedin_post

Create a LinkedIn post on behalf of a connected profile. By default the post is saved as a 'draft' in the LinkedIn Posts page so the user can review/edit it before publishing. Set auto_publish=true to publish immediately — that path still respects the user's MCP human-in-the-loop setting (when approval is required, the post stays as a draft and the user must publish it from the LinkedIn Posts page in the app). A random 30–180 s anti-detection delay is applied before the publish call. Attachments are not supported via MCP — add images in the in-app post editor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesPost text (LinkedIn limit ~3000 chars). Plain text with line breaks; LinkedIn does not render Markdown.
profile_idYesUUID of the user_profile to post from (must have a connected LinkedIn account)
auto_publishNoIf true, publish immediately (subject to MCP approval setting). Default false = save as draft for in-app review.
external_linkNoOptional URL to attach as a link preview
as_organizationNoOptional LinkedIn organization URN to post as a company page (must be admin in LinkedIn)

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=false, etc.), the description discloses important behaviors: default draft, auto_publish respects MCP setting, and a random anti-detection delay before publishing. These details help the agent understand what happens during invocation.

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 concise at four sentences, with the main action stated first. Each sentence provides necessary detail without redundancy. The structure is logical and easy to parse.

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?

The description covers the core workflow, limitations (no attachments), and edge cases (MCP approval). While no output schema exists, the description doesn't elaborate on return values, but that is acceptable given the tool creates a resource. The description is sufficiently complete for an agent to use it correctly.

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

Parameters4/5

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

The input schema covers all parameters with descriptions, but the tool description adds context: text is plain text without Markdown, profile_id must be a connected LinkedIn account, auto_publish behavior and interaction with MCP approval, external_link as optional URL, and as_organization requiring admin rights. The anti-detection delay is also noted.

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's purpose: creating a LinkedIn post on behalf of a connected profile. It distinguishes between saving as draft and immediate publishing, and notes that attachments are not supported. This differentiates it from sibling tools like send_linkedin_message.

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 default behavior (draft) and the auto_publish option with its interaction with MCP approval settings. While it doesn't explicitly state when not to use this tool, it provides sufficient context for typical usage.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among search tools (search_job_postings, search_google_xray, search_linkedin_people, search_web) and messaging tools (send_connection_request, send_linkedin_message, reply_to_chat). However, detailed descriptions clarify the differences.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (e.g., add_contacts_to_campaign, list_campaigns). No mixing of conventions.

Tool Count3/5

48 tools is high but justifiable given the broad domain (LinkedIn outreach, CRM, campaigns, job postings, etc.). However, some tools could be consolidated (e.g., multiple search tools).

Completeness5/5

The tool set covers the entire workflow: searching, connecting, messaging, campaign management, CRM operations (fields, stages, tasks, notes), job postings, and posting. No obvious gaps.

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