How to set up Model Context Protocol local environment

Model Context Protocol (MCP) is an open standard, created by Anthropic and now developed as a community open-source project, for connecting an LLM to external tools and data — instead of writing a bespoke integration for every editor or app you want a model to work inside, you write one MCP server and any MCP-compatible client (Claude Desktop, an IDE plugin, a custom app) can talk to it. Running it locally means the server process — and whatever local files, databases, or scripts it touches — never leaves your machine.

Step-by-Step Implementation

1. Initialize MCP Server: Create a project using Node.js or Python with the official SDK libraries.

2. Register Custom Tools: Declare tools, schemas, and resource resolvers inside the server's manifest.

3. Link to Client Editor: Add the server's execution path and environment keys to your desktop configuration file.

# Custom Model Context Protocol (MCP) tool server
from mcp.server.fastmcp import FastMCP
import sys

# Initialize FastMCP server instance
mcp = FastMCP("Local Workspace Assistant")

@mcp.tool()
def read_system_log(lines: int = 50) -> str:
    """Reads local system execution logs for debugging agent behaviors."""
    try:
        with open("workspace_logs.txt", "r") as f:
            log_data = f.readlines()
        return "".join(log_data[-lines:])
    except Exception as e:
        return f"Error reading logs: {str(e)}"

if __name__ == "__main__":
    # Run server using Stdio transport for editor integration
    mcp.run()

Once the server is registered with your client's config file, the model can call read_system_log (or any tool you register) the same way it would call a built-in tool — the client handles spawning the server process and routing JSON-RPC messages back and forth over stdio.