mcp-linkedin

MCP.Pizza Chef: adhikasp

The mcp-linkedin server is a Model Context Protocol (MCP) server that provides structured tools to interact with LinkedIn's Feeds and Job API. It leverages an unofficial LinkedIn API to enable real-time access and manipulation of LinkedIn data such as feeds and job postings. This server facilitates integration of LinkedIn context into AI workflows, allowing models to fetch, analyze, and act on LinkedIn content programmatically. Installation is streamlined via Smithery, supporting clients like Claude Desktop. Users should note that it uses an unofficial API, so usage is at their own risk.

Use This MCP server To

Fetch LinkedIn feed updates for real-time analysis Retrieve job postings from LinkedIn for job matching Integrate LinkedIn data into AI-driven recruitment tools Automate LinkedIn feed monitoring for trends Enable AI agents to interact with LinkedIn content Aggregate LinkedIn job listings for market research

README

MCP LinkedIn

A Model Context Protocol (MCP) server that provides tools to interact with LinkedIn's Feeds and Job API.

This is using unofficial LinkedIn API via Linkedin-api. Use at your own risk.

Smithery Badge mcp-linkedin MCP server

Installing via Smithery

To install LinkedIn Interaction Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install mcp-linkedin --client claude

Configuration

{
    "mcpServers": {
        "linkedin": {
            "command": "uvx",
            "args": ["--from", "git+https://github.com/adhikasp/mcp-linkedin", "mcp-linkedin"],
            "env": {
                "LINKEDIN_EMAIL": "your_linkedin_email",
                "LINKEDIN_PASSWORD": "your_linkedin_password"
            }
        }
    }
}

Sample usage

Using mcp-client-cli

Get feed posts

$ llm whats happening in linkedin feed? Do not mention names

I'll retrieve the recent LinkedIn feed posts without mentioning any specific names.

Tool Calls:
  get_feed_posts
  Args:
    limit: 10

Based on the LinkedIn feed, here are the key highlights without mentioning specific names:

1. Data Science & Cybersecurity Insights:
   - A post about a massive dataset from a major tech company
   - Includes 13 million pieces of evidence
   - Covers SIEM telemetry from 6,100 organizations
   - Represents 441 MITRE ATT&CK techniques

2. DevOps Community Event:
   - A local DevOps meetup was held at a tech company office
   - Discussed topics like AWS Lambda, Kubernetes scaling, Terraform
   - Highlighted the importance of networking and continuous learning
   - Shared experiences of transitioning between tech roles

3. Linux Engineering Perspectives:
   - Insights into what makes a great Linux engineer
   - Key skills include:
     * Command-line troubleshooting
     * Process management
     * Networking fundamentals
     * System architecture understanding
     * Task automation
     * Shell scripting

4. Professional Networking:
   - Some posts about job seeking
   - Discussions about career transitions
   - Sharing professional experiences and learnings

5. Tech Community Reflections:
   - Posts about inspirational figures
   - Discussions on professional growth
   - Reflections on learning and personal development

The feed showcases a mix of technical insights, professional networking, career development, and community engagement across various tech domains.

Search for jobs

$ llm search for 3 data engineer jobs in jakarta. For each job check if it a good match for me by analyzing it against my resume in file resume.md. Do not mention company name in your result but only generic area where the company business is about

I'll help you find data engineer jobs in Jakarta and analyze them against your resume. I'll break this down into steps:

1. First, I'll search for data engineer jobs in Jakarta
2. Then, I'll read your resume
3. Finally, I'll analyze the job matches

Let's start by searching for jobs:

Tool Calls:
  search_jobs
  Args:
    keywords: data engineer
    location: Jakarta
    limit: 3


Now, I'll read your resume:

Tool Calls:
  read_file
  Args:
    path: resume.md
Secure MCP Filesystem Server running on stdio
Allowed directories: [ '/home/adhikasp', '/mnt/e' ]


Now, I'll analyze the jobs against your resume:

1. Job in Financial Technology Sector (Lead Backend Engineer):
   - Match Score: 85%
   - Strengths:
     * Requires experience in developing scalable microservices (aligns with your Grab experience)
     * Strong focus on cloud infrastructure (AWS, GCP) - matches your cloud skills
     * Requires experience with event-driven architecture and message brokers
     * Requires Go or Python programming (your proficient languages)
   - Potential Gaps:
     * Specific focus on credit risk and fraud detection might require additional domain knowledge
     * Emphasis on team leadership (6+ years experience leading teams)

2. Job in Financial Technology Sector (DevOps Engineer):
   - Match Score: 75%
   - Strengths:
     * Requires cloud platform experience (AWS, GCP)
     * Needs containerization skills (Docker, Kubernetes)
     * Requires scripting in Python and Bash
     * Needs infrastructure as code experience
   - Potential Gaps:
     * More DevOps focused compared to your backend engineering background
     * Specific monitoring tools might require additional learning

3. Job in Enterprise Collaboration Software (Solution Engineer):
   - Match Score: 60%
   - Strengths:
     * Requires strong communication skills
     * Technical consulting background
     * Experience with enterprise software
   - Potential Gaps:
     * Less technical compared to your current role
     * More sales-oriented solution engineering
     * Less hands-on technical development

Recommendation:
The first job (Lead Backend Engineer) appears to be the best match for your skills and experience. It closely aligns with your current work, particularly in terms of backend development, cloud infrastructure, and working with distributed systems. The role's requirements in Go/Python, microservices, and event-driven architecture are very compatible with your professional background.

Would you like me to elaborate on any of these job analyses or provide more detailed insights?

mcp-linkedin FAQ

How do I install the mcp-linkedin server?
You can install it automatically using Smithery CLI with the command: npx -y @smithery/cli install mcp-linkedin --client claude.
Is the LinkedIn API used by mcp-linkedin official?
No, it uses an unofficial LinkedIn API via the linkedin-api library, so use it at your own risk.
Can I use mcp-linkedin with LLMs other than Claude?
Yes, while installation instructions mention Claude, the MCP protocol supports multiple LLM providers like OpenAI, Claude, and Gemini.
What environment configuration is needed to run mcp-linkedin?
The server requires environment variables and command arguments as specified in its configuration JSON, typically managed via MCP host or Smithery.
Does mcp-linkedin support real-time LinkedIn feed updates?
Yes, it provides tools to interact with LinkedIn feeds in real-time for dynamic context integration.
Is there any risk in using mcp-linkedin?
Since it uses an unofficial API, there may be risks including API changes or access restrictions imposed by LinkedIn.
How does mcp-linkedin integrate with AI workflows?
It exposes LinkedIn data as structured context to MCP clients, enabling LLMs to reason over and act on LinkedIn content.
Where can I find the source code for mcp-linkedin?
The source code is available on GitHub at https://github.com/adhikasp/mcp-linkedin.