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LinkedIn MCP Server

LinkedIn MCP Server

by AndresMinakata
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Get Company Posts

get_company_posts
Read-only

Retrieve recent LinkedIn posts from a company feed using its name; optionally scroll to load older posts.

Instructions

Get recent posts from a company's LinkedIn feed.

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": {
      "company_name": "<company_name>"
    },
    "name": "get_company_posts"
  }
}

Input Schema

NameRequiredDescriptionDefault
max_scrollsNoMaximum scroll-to-bottom iterations to load more posts. Default (None) uses 10. Increase to read further back in the feed.
company_nameYesLinkedIn company name (e.g., "docker", "anthropic", "microsoft"). A full company URL is accepted too and is reduced to the slug.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.26.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds only the vague word 'recent' and says nothing about needing an active session (implied by the close_session sibling), how far back the feed is read, or how results are ordered — despite a scroll-budget parameter existing.

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?

A single tight sentence with no wasted words and the resource front-loaded. It is efficient, though its brevity is closer to under-specification than to optimal information density.

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 values need not be explained. However, for a session-based scraping tool the description omits the prerequisites (active session) and the sibling-routing context an agent needs, leaving it only minimally viable.

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%: company_name and max_scrolls are both fully documented in the schema, including the default of 10 scrolls and URL-to-slug reduction. The description adds no parameter meaning beyond that, so the baseline of 3 applies.

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?

States a specific verb and resource ('Get recent posts') and scopes it to 'a company's LinkedIn feed', which implicitly separates it from the personal-feed and search siblings. It does not, however, name get_feed or search_posts explicitly, so the differentiation must be inferred.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no when-to-use guidance at all. With siblings get_feed and search_posts present, an agent is left to guess whether this is the right tool for reading a company's activity versus searching or reading its own feed.

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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