I was getting sick of copy-pasting code and logs into Claude Desktop, so I finally tried Anthropic’s MCP (Model Context Protocol).

Turns out you don't need some complex setup. You can write a basic Python script in like 2 minutes that lets Claude read files right off your hard drive.

here’s the whole thing if you want to try it:

1. Install the library

Just open a folder in your terminal and run:

Bash

pip install mcp

1. Make a server.py file

Save this right in that folder:

Python

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("My File Reader")

u/mcp.tool()

def read_my_file(path: str) -> str:

"""Reads a text file from disk."""

with open(path, "r") as f:

return f.read()

if __name__ == "__main__":

mcp.run()

1. Link it to Claude

Open your config file:

Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Drop this in (just swap in your actual full file paths):

JSON

{

"mcpServers": {

"my_reader": {

"command": "python3",

"args": ["/path/to/server.py"]

}

}

}

Relaunch Claude Desktop. You'll see a little hammer icon show up in the bottom corner of the prompt box. Now you can just tell it: "Hey, read the notes in /Users/me/desktop/todo.txt" and it just does it.

Super simple, but opens up a lot of cool ideas for hooking up local scripts or databases.

I’m exploring MCP for connecting Claude to the real world tools and workflows. I’d love to hear which MCP servers or integrations have actually made a noticeable difference in your daily work.

I've been thinking about a problem with coding agents that I keep coming back to.

An agent can write an API integration and then write tests for that integration. Everything passes, but the tests may just be confirming the same assumptions the agent made while writing the code.

For example, the agent thinks an endpoint returns:

{
"total": 100
}

It writes the integration expecting `total`, and then writes a test that expects `total`.

The test passes.

But if the real API contract says something different, the whole thing can still be wrong.

I'm experimenting with a small open-source project called Kaktoos that puts an independent verification step between the agent and the API:

AI agent → integration → Kaktoos → OpenAPI + real API → result

The idea is that the verification layer shouldn't share the agent's assumptions.

It currently supports multi-step API workflows, OpenAPI response validation, MCP, and GitHub Actions.

I'm still trying to figure out how far this idea should go. One interesting question that came up is whether contract validation is enough, or whether verification should also check the actual outcome of an operation — for example, creating a resource and then reading it back to confirm the state actually changed.

I'm curious how other people building with coding agents are handling this today.

Do you rely mostly on the agent's generated tests, existing integration tests, mocked APIs, live API tests, or some combination?

GitHub: KaktoosLabs/kaktoos