quinnjr/linkedin-mcp — A comprehensive Model Context Protocol (MCP) server for LinkedIn API - manage profiles, posts, conne…

Description and use

**Brief Summary**
quinnjr/linkedin‑mcp is an open‑source Model Context Protocol (MCP) server that wraps the LinkedIn API, exposing a unified, AI‑friendly interface for managing profiles, posts, connections, skills, education, certifications, and more. Built in TypeScript, it lets AI agents interact with real LinkedIn data through a standard protocol, simplifying the creation of automated LinkedIn workflows.

**Value**

• **Standardized AI‑to‑Tool bridge** – By implementing MCP, the project provides a consistent contract that any AI assistant can use, eliminating the need to write custom LinkedIn SDK calls for each agent.
• **Rich LinkedIn feature set** – All major LinkedIn entities (profiles, posts, connections, etc.) are covered, enabling end‑to‑end automation such as content publishing, network growth, and skill verification.
• **Open‑source and extensible** – The TypeScript codebase can be forked or extended to add custom business logic, compliance checks, or additional LinkedIn endpoints.

**Practical Adoption Path**

1. **Prototype** – Clone the repo, run the provided Docker/CLI setup, and point the MCP server at a LinkedIn developer app’s credentials.
2. **Integrate** – Connect your AI agent (e.g., LangChain, AutoGPT, or a custom LLM orchestrator) to the MCP endpoint using the published OpenAPI spec or SDK.
3. **Test & Secure** – Validate request/response flows in a sandbox LinkedIn account, add rate‑limit handling and OAuth token refresh logic.
4. **Deploy** – Containerize the server and roll it out to a staging environment behind your corporate API gateway; configure monitoring and audit logs.
5. **Production** – Scale the service (K8s, serverless) and enforce enterprise security policies (scoped OAuth scopes, secret management).

**Production Readiness**

• **Recent activity**: Updated on 2026‑06‑26 with active issue handling, indicating a healthy maintainer presence.
• **Community signals**: 25 stars, 2 forks, and 18 relevant topics show modest but engaged interest.
• **Technical maturity**: Written in TypeScript with clear API/SDK/CLI entry points, making integration straightforward for modern stacks.
• **Risk considerations**: License compliance and a deeper security audit (OAuth token handling, dependency vulnerabilities) are still required before a mission‑critical rollout, but no major red flags have been identified.

Overall, quinnjr/linkedin‑mcp is a solid OSS candidate for pilots and can be hardened for production with standard security and DevOps practices.

Problem

quinnjr/linkedin-mcp helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

A comprehensive Model Context Protocol (MCP) server for LinkedIn API - manage profiles, posts, connections, skills, education, certifications, and more through AI agents

Source