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Provides tools to interact with LinkedIn Feeds and Job API via MCP
Configuration
View docs{
"mcpServers": {
"adhikasp-mcp-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"
}
}
}
}This MCP server provides tools to interact with LinkedIn's Feeds and Job API using an MCP client. It lets you fetch feed posts and search for jobs programmatically, enabling seamless integration with your workflows while keeping authentication and API access managed through MCP tooling.
You use this server by connecting it to an MCP client and invoking its available tools. The server exposes functionality to retrieve recent LinkedIn feed posts and to search for jobs based on your criteria. When you run a client command, specify your desired action (for example, get feed posts or search for jobs) and the client will perform the corresponding tool call behind the scenes. The client will surface results and highlights without exposing sensitive authentication details.
Prerequisites you need before installing:
- Node.js and npm installed on your machine.
- Access to the internet to fetch packages.
- npx available with your npm installation.npx -y @smithery/cli install mcp-linkedin --client claudeConfiguration for the LinkedIn MCP server is provided as a local stdio process that you run through your MCP client. The server starts with a runtime command and environment variables as shown in the config example.
{
"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"
}
}
}
}Fetches recent LinkedIn feed posts controlled by the client, returning a summary of items and highlights from the user's LinkedIn feed.
Searches for job postings based on provided keywords and location, returning a curated list of matching roles with key details.
Reads local files when needed by the client to compare resumes or provide contextual analysis for matching tasks.