Zbigniewtomanek My MCP Server

The Zbigniewtomanek My MCP Server is a lightweight, Python‑based implementation of the Model Context Protocol (MCP) that exposes a set of file‑system and shell‑execution tools to Large Language Models (LLMs) such as Claude Desktop, GPT‑4o, Gemini, and any other MCP‑compatible client. By providing a standardized interface for local system interaction, the server removes the friction that normally separates an LLM from the developer’s workspace. Developers can now ask an assistant to read, edit, or search files and run arbitrary commands without leaving their IDE or chat window.

What Problem Does It Solve?

In most AI workflows, LLMs are confined to the data they receive in prompts or through external APIs. When a developer needs to inspect source code, run tests, or manipulate files, the assistant must rely on separate tooling or manual copy‑paste. This server bridges that gap by offering a secure, sandboxed bridge between the LLM and the host machine. It eliminates the need for custom scripts or third‑party integrations, allowing a single, well‑defined set of commands to be reused across projects and teams.

Core Capabilities

• Command Execution – runs arbitrary shell commands and streams the output back to the model, enabling dynamic analysis, test execution, or environment interrogation.
• File Viewing – lets the model read file contents, optionally limited to a range of lines for concise context.
• Pattern Search – performs regex searches across files, returning matches that can be used for code review or refactoring suggestions.
• File Editing – supports targeted string replacements and line‑based modifications, allowing the model to propose or apply code changes directly.
• File Writing – appends or overwrites file contents, giving the assistant a full read‑write cycle over the project’s files.

These tools are wrapped in a simple JSON API that follows MCP specifications, ensuring compatibility with any client that implements the protocol.

Real‑World Use Cases

• Automated Code Refactoring – Ask the assistant to find and replace a legacy API call across multiple files, then review the diff before committing.
• On‑the‑Fly Testing – Run unit tests or linting tools from within the chat and get instant feedback on failures.
• Rapid Prototyping – Generate boilerplate code, write it to a file, and immediately run a build command—all without leaving the assistant.
• Documentation Generation – Search for comments, extract them into a report, and write the summary to a markdown file.
• Security Audits – Execute static analysis tools and parse their output to surface vulnerabilities.

Integration into AI Workflows

The server is launched via a single command line and registered in the MCP client configuration (e.g., Claude Desktop). Once connected, any supported tool becomes a first‑class action that the model can invoke as part of its reasoning process. Because MCP enforces a 1:1 client‑server relationship, each session is isolated, reducing the risk of unintended side effects. Developers can toggle shortcuts or disable specific tools in the configuration to fine‑tune security.

Unique Advantages

• Vendor Agnostic – The same server works with any MCP‑compatible LLM, making it future‑proof against shifts in AI provider landscapes.
• Python 3.10+ Friendly – Built on modern Python tooling (uv, FastMCP), it integrates seamlessly into existing development environments.
• Security‑First Design – Tools are intentionally limited to file operations and shell commands, with no direct access to network sockets or privileged processes.
• Extensible Architecture – Adding a new tool is as simple as defining a function and exposing it through the MCP interface, encouraging community contributions.

In short, Zbigniewtomanek’s MCP server turns a generic LLM into a powerful, context‑aware assistant that can read, modify, and act on the developer’s codebase in real time, dramatically accelerating iteration cycles and reducing context switching.