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Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-hritik003-linkedin-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
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# MCP Server for LinkedIn [](https://smithery.ai/server/@Hritik003/linkedin-mcp) A Model Context Protocol (MCP) server for linkedin to apply Jobs and search through feed seamlessly. This uses Unoffical [Linkedin API Docs](https://linkedin-api.readthedocs.io/en/latest/api.html) for hitting at the clients Credentials. ## Features 1. **Profile Retrieval** Fetch user profiles using `get_profile()` function Extract key information such as `name`, `headline`, and `current position` 2. **Job Search** - Advanced job search functionality with multiple parameters: - Keywords - Location - Experience level - Job type (Full-time, Contract, Part-time) - Remote work options - Date posted - Required skills - Customizable search limit 3. **Feed Posts** - Retrieve LinkedIn feed posts using `get_feed_posts()` - Configurable limit and offset for pagination 4. **Resume Analysis** - Parse and extract information from `resumes (PDF format)` - Extracted data includes: - Name - Email - Phone number - Skills - Work experience - Education - Languages --- # Configuration After cloning the repo, adjust the `<LOCAL_PATH>` accordingly ```python { "linkedin":{ "command":"uv", "args": [ "--directory", "<LOCAL_PATH>", "run", "linkedin.py" ] } } ``` --- # Usage I have been testing using [MCP-client](https://github.com/chrishayuk/mcp-cli) and found as the best one for testing your `MCP-Servers`.
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