Automating Data Analysis with MCP Servers

I am currently exploring new ways to automate my data analysis workflows using Vibe Coding. By building custom tools, I can significantly streamline the process of handling and analyzing scientific data.

Scaling Tools for Collaboration

When it comes to sharing these tools with my colleagues, I prioritize accessibility. For team-based collaboration, I deploy these tools as Domino Apps. This allows others to interact with the automated workflows through a user-friendly web interface without needing to worry about the underlying infrastructure.

Local Empowerment via MCP Servers

For my personal local environment, I have taken a different approach to ensure my projects are “AI-ready.” I have converted all my projects into MCP (Model Context Protocol) Servers. This allows these projects to act as specialized tools that AI agents can directly leverage.

How to Build an MCP Server

The process of transforming a project into an MCP server is straightforward:

1. Add a Server Entry Point: Create a server.py file within the project.
2. Install the MCP Library: Use the MCP server library to handle the protocol communication.
3. Expose Functionality: Define and expose specific tools or functions that the project can perform.

By doing this, the entire project becomes a structured resource that can receive instructions and provide data to an AI agent.

Integrating with AI Agents

To make these MCP servers available to AI coding agents like Claude Code or Codex, they need to be registered in the agent’s configuration files.

For Claude Code, this involves adding the server details to the .claude.json configuration file. Once registered, the AI agent “knows” these tools exist and can call upon them while working on other projects, effectively using my local codebase as a set of sophisticated data analysis plugins.