About Resume MCP Server
Resume MCP Server turns a personal resume into an interactive MCP server deployed on Cloudflare Workers. It is for AI assistants querying a person's experience, skills, and projects, with tools for filtering, searching, and formatting resume data.
Key features
- 01Query personal and education details
- 02Filter work experience by company
- 03Filter projects by technology
- 04Retrieve skills by category
- 05Search and format resume data
Use cases
- 01Answer questions about a person's career
- 02Find resume projects by technology
- 03Search resume keywords and generate a formatted version
MCP support
The MCP server exposes tools for querying personal details, work experience, projects, skills, open-source work, and searching or formatting the resume. It can run on Cloudflare Workers and connect through an SSE endpoint.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEResume MCP Server
This project transforms my resume into an interactive Model Context Protocol (MCP) server deployed on Cloudflare Workers. When AI assistants connect to this MCP server, they can query my professional experience, skills, and projects in a structured way.
Features
- MCP Compliant: Follows the Model Context Protocol specification
- Interactive Resume: AI assistants can query specific parts of my resume
- Cloudflare Deployment: Globally available with low latency
- Structured Data: Organized resume information for natural conversations
Available Tools
- getBasicInfo: Get basic personal and educational information
- getWorkExperience: Get detailed work experience, optionally filtered by company
- getProjects: Get details about personal projects, optionally filtered by technology
- getSkills: Get technical skills by category
- getOpenSourceWork: Get details about open source contributions
- searchResume: Search for keywords across the entire resume
- generateFormattedResume: Generate a formatted version of the resume
Setup and Deployment
Prerequisites
- Node.js and npm
- Cloudflare account
- Wrangler CLI (
npm install -g wrangler)
Local Development
- Clone this repository
- Install dependencies:
npm install - Start the development server:
npm run dev - Your MCP server will be available at
http://localhost:8787/sse
Testing with MCP Inspector
You can test your MCP server using the MCP Inspector tool:
npx @modelcontextprotocol/inspector@latest
Open your browser to http://localhost:5173 and connect to your MCP server URL.
Deployment to Cloudflare
Deploy your MCP server to Cloudflare Workers:
npm run deploy
After deployment, your MCP server will be accessible at https://resume-mcp-server.your-username.workers.dev/sse.
Configuring Claude to Use Your MCP Server
Update your Claude Desktop configuration to point to your MCP server:
{
"mcpServers": {
"resume": {
"command": "npx",
"args": [
"mcp-remote",
"https://resume-mcp-server.your-username.workers.dev/sse"
]
}
}
}
Restart Claude Desktop after updating your config file to load the MCP Server.
Project Structure
src/index.ts: Main application entry point and MCP tool definitionssrc/resume-data.ts: Structured resume datawrangler.toml: Cloudflare Workers configurationpackage.json: Project dependencies and scriptstsconfig.json: TypeScript configuration
Adding Authentication (Optional)
If you want to add authentication to your MCP server, follow these steps:
-
Create a new project using the GitHub OAuth template:
npm create cloudflare@latest -- resume-mcp-server-auth --template=cloudflare/ai/demos/remote-mcp-github-oauth -
Create GitHub OAuth Apps for both local development and production.
-
Add the client ID and secret to your environment variables.
-
Update your MCP server code to use the OAuth provider.
License
MIT
Author
Chisom Chibogu
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.