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Mayra
Mayra

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Unifying the Ecosystem: Building and Deploying an MCP Server for Modern Clients

#python #tutorial #cloud #architecture

The Power of the MCP Server
The modern development landscape is diverse, requiring seamless communication between tools like AI assistants (e.g., Claude), IDEs (e.g., VSCode), and custom CLI tools. This is where the Message Control Protocol (MCP) Server steps in, acting as a unified communication broker.

The MCP Server provides a single, standardized endpoint for command execution and data exchange. This article provides a concise guide to building a basic MCP Server using Python and deploying it to Google Cloud Run, demonstrating its immediate utility for diverse MCP Clients.

Server Implementation Core (Python & Flask)
We use Python and the Flask framework to create a simple HTTP service that listens for POST requests containing our MCP message format (JSON).

_The MCP Message Protocol
_All client-server communication adheres to a simple JSON structure:

{
  "client_id": "vscode-ext-001",
  "command": "GET_STATUS",
  "payload": {
    "target_resource": "db_connection"
  }
}
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_Flask Handler Snippet
_The core logic processes the incoming command and returns a structured response:

from flask import Flask, request, jsonify

app = Flask(__name__)

def process_command(data):
    """Handles business logic based on the MCP command."""
    command = data.get("command")

    if command == "GET_STATUS":
        # Simulates checking resource status
        resource = data.get("payload", {}).get("target_resource", "unknown")
        status = "OK" if resource == "service-A" else "ERROR"
        return {"status": "success", "response": f"Status for {resource}: {status}"}

    return {"status": "error", "response": f"Unknown command: {command}"}

@app.route('/mcp/control', methods=['POST'])
def mcp_handler():
    try:
        data = request.get_json()
        if not data:
            return jsonify({"status": "error", "response": "Invalid JSON"}), 400

        response = process_command(data)
        return jsonify(response), 200

    except Exception as e:
        return jsonify({"status": "fatal_error", "response": str(e)}), 500

if __name__ == "__main__":
    import os
    port = int(os.environ.get("PORT", 8080))
    app.run(host="0.0.0.0", port=port)
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Cloud Run Deployment (Serverless)
We containerize the Python server using a Dockerfile and deploy it to Google Cloud Run for automatic scalability and high availability.

Dockerfile Summary
The Dockerfile sets up the Python environment, installs dependencies from requirements.txt, and runs the Flask application on the port specified by Cloud Run.

# (Omitted full Dockerfile for brevity)
# Key steps: FROM python:3.10-slim, COPY requirements.txt, RUN pip install, CMD ["python", "app.py"]
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Deployment Steps
After building the Docker image and pushing it to a container registry, the deployment is executed:

# 1. Build: gcloud builds submit --tag gcr.io/[PROJECT-ID]/mcp-server
# 2. Deploy: gcloud run deploy mcp-server --image gcr.io/[PROJECT-ID]/mcp-server --platform managed --allow-unauthenticated
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The deployed service provides a public URL (e.g., https://mcp-server-xyz.a.run.app) for all clients to connect.

MCP Client Connectivity
Our deployed server is now ready to serve any client adhering to the MCP structure.

Client Example 1: AI Assistant (e.g., Claude)
An AI assistant can be configured to use our MCP Server URL as an external tool. When a developer asks, "What's the status of Service-A?", the AI translates this into the GET_STATUS command and sends it to the server. The server responds with the status, which the AI then converts back into a natural language response: "Service-A is currently reporting an OK status."

Client Example 2: VSCode Extension
A VSCode extension can connect to the MCP Server to trigger actions. For instance, a button press in the IDE might send an EXECUTE_JOB command to clean up local resources or initiate a deployment pipeline. The server handles the logic, and the extension confirms success to the user, enhancing developer workflow directly within the IDE.

Conclusion
By implementing the MCP Server on a serverless platform like Google Cloud Run, we have established a scalable and flexible control layer. This unified protocol simplifies tool integration, paving the way for more powerful and interconnected development ecosystems.

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