About MCP Server - Job Assistant
MCP server concept that connects to Gmail to parse recruiter emails, interview schedules, and follow-ups for job applicants. The README describes a project idea and setup requirements, not a complete feature set.
Key features
- 01Gmail email parsing
- 02Recruiter outreach identification
- 03Interview schedule identification
- 04Follow-up identification
Use cases
- 01Review recruiter outreach
- 02Track interview scheduling emails
- 03Find job application follow-ups
MCP support
It is described as an MCP server intended to connect to Gmail and support job application management. The README shows installation in Claude Desktop.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Server - Job Assistant
Project Idea: Develop an MCP server that connects to a Gmail account to automatically read and parse emails, identifying recruiter outreach, interview schedules, and follow-ups for streamlined job application management.
Reference Link: https://github.com/modelcontextprotocol/python-sdk?tab=readme-ov-file
Requirements:
- UV
- Claude Desktop
First time - For UV enabled project creation uv init mcp-server-demo cd mcp-server-demo uv add "mcp[cli]"
You can install this server in Claude Desktop and interact with it right away by running: mcp install server.py
✅ 1. Enable the Gmail API Go to the Google Cloud Console. Create a new project (or select an existing one). Go to APIs & Services > Library and enable the Gmail API. Go to APIs & Services > Credentials: Create OAuth 2.0 Client ID credentials. Download the credentials.json file.
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.