A
licenseB
qualityD
maintenanceEnables parsing and extracting structured data from resumes using the Rchilli API. Supports resume parsing from public URLs or binary data in base64 format.
2
MIT
Resume Parser MCP
by Acidlambunk
Uses Google's Gemini AI model to parse unstructured resume text and convert it into structured JSON format with extracted skills, experience, education, and projects
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Resume Parser MCPparse this resume text into structured JSON"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Json to Json Resume-Parser MCP
This is a resume parser MCP that takes a random json input: { "raw_text": "John Doe, Software Engineer with 3 years of experience in Python, AWS, Docker. Worked at Acme Inc from 2020 to 2023..." } Into a more sturctured output: { "skills": ["Python", "AWS", "Docker"], "experience": [ {"company": "Acme Inc", "role": "Software Engineer", "years": "2020-2023"} ], "education": [], "projects": [] } using gemini model..
Clone the repository:
git clone https://github.com/Acidlambunk/Resume-Parser-MCP.git
cd testInstall uv and setup venv use google and install uv setup venv with
python -m venv (name) source (name)/bin/activateInstall dependencies:
uv pip install -r requirements.txtSet up environment variables:
create a new
.envfile and add your API keys:GEMINI_API_KEY= GEMINI_MODEL=gemini-2.0-flash
Run the API locally:
uv run mcp dev main.py
This server cannot be deployed
Maintenance
ActivityInactive
ResponsivenessNo issues
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