LinkedIn MCP Runner

Overview

The LinkedIn MCP Runner is a dedicated Model Context Protocol (MCP) server that bridges GPT‑based assistants—Claude, ChatGPT, and others—with a user’s own LinkedIn activity. By exposing public post data through the MCP interface, it lets AI agents answer contextual questions such as “Which of my last posts gained the most engagement?” or “What tone does my writing actually convey?” without requiring manual data uploads. This seamless connection eliminates the friction of constantly refreshing a language model’s knowledge base, allowing developers to treat LinkedIn as an ever‑updating source of truth for content strategy.

What the Server Does

At its core, the server authenticates a user via LiGo’s OAuth flow and then serves two main capabilities: context retrieval and actionable generation. When a prompt is issued, the MCP client queries the server for recent posts, engagement metrics, and stylistic fingerprints. The AI then uses this data to generate insights, rewrite drafts in the user’s voice, or brainstorm fresh post ideas—all while staying within the same conversational thread. This tight coupling between data and generation makes the assistant behave like a personal strategist who knows your historical performance.

Key Features

• Real‑time LinkedIn analytics: Pull the latest 50 posts, likes, comments, and shares directly from the user’s profile.
• Voice‑aligned rewriting: Feed raw text to the assistant and receive a polished LinkedIn‑ready version that mimics your proven tone.
• Performance analysis: Quickly identify which content types resonate most, enabling data‑driven iteration.
• Cross‑platform support: Works natively with Claude (desktop) and ChatGPT’s CustomGPT, so developers can choose their preferred environment.
• Public showcase: The MCP Leaderboard displays the latest posts generated through the server, offering a live demo and SEO benefits via backlinks.

Real‑World Use Cases

• Content creators & founders: Automate the drafting process by letting the AI rewrite ideas in their established voice, saving time while maintaining authenticity.
• Social media managers: Quickly analyze engagement trends across a brand’s LinkedIn presence and generate tailored post suggestions.
• Marketing teams: Integrate the MCP Runner into internal workflows to keep AI assistants up‑to‑date with campaign results without manual data feeds.
• Developers building custom assistants: Use the MCP endpoints to embed LinkedIn insights into bespoke tools, dashboards, or chatbots.

Integration with AI Workflows

Developers can incorporate the LinkedIn MCP Runner into existing AI pipelines by simply installing the MCP client (for Claude) or enabling the CustomGPT integration. Once authenticated, any prompt that references LinkedIn data automatically routes through the MCP server, ensuring that every response reflects the latest public activity. This eliminates the need for periodic data exports or manual updates, allowing developers to focus on higher‑level logic and user experience.

Unique Advantages

The LinkedIn MCP Runner stands out by offering native, privacy‑respecting access to a user’s public LinkedIn content without exposing raw data to the AI model. Its tight coupling with LiGo’s broader platform—encompassing commenting, analytics, and CRM—provides a unified ecosystem for LinkedIn professionals. Moreover, the public leaderboard creates a self‑reinforcing loop of visibility and SEO benefits, giving users an incentive to share their AI‑generated posts while showcasing the server’s capabilities.