Integrating an MCP server workflow wise or not?

I made some hefty .gh scripts to simulate the behaviour of kites for making energy
My automation / looping / runtime control skills are flakey at best.

It took ages to coordinate all the actions involved in running the script, recording results resetting, repeating… I got bored

I’m one of those AI shiny toy bandwagon folk now and
I started finding uses for MCP servers while playing AI in VS Code

So far AI has been useless for developing my kite simulations
But I can now run reasoning models locally and they’re able to use these MCP servers / tools / clients to perform tasks locally and integrate with other software - more standard reliable code services.

So I’m looking for your advice -
Do I let the AI guide me into doing it’s bidding and following the approach described below for developing a two way communication between gh and a computer user AI with a reasoning model so that it can run tests over and over for me ? OR is this doomed/pointless from the start? Or am I going to miss out on all the .gh fun by giving the computer the tools

Am I hopelessly optimistic and need much tighter defined mission specification parameters ?

Here’s the plan according to the machines

To enable an AI to interact with RhinoPython components in Grasshopper locally, follow this structured approach using an MCP (Message Control Protocol) server:

  1. Architecture Overview
    MCP Server: Embedded within Rhino via a RhinoPython script, handling TCP communication and Grasshopper interaction.

AI Client: External client sending JSON commands to the server.

Workflow: AI → Client → MCP Server → Grasshopper → Results → AI.

  1. Components & Tools
    MCP Server (Inside Rhino)
    Technology: RhinoPython (IronPython) with .NET System.Net.Sockets.

Role:

Listen for TCP connections.

Process JSON commands (component ID, inputs).

Manipulate Grasshopper components and return outputs.

AI Client
Language: Python (or any language supporting sockets).

Role: Send commands to the MCP server and handle responses.

Grasshopper Setup
Ensure components have unique IDs/names for easy access.

Use a .gh file with predefined logic for the AI to control.

  1. Step-by-Step Implementation
  2. MCP Server in RhinoPython
    python
    Copy
    import clr
    import System
    from System.Net import Sockets
    from System.IO import StreamReader, StreamWriter
    from System.Text import Encoding
    from System.Threading import Thread, ThreadStart

clr.AddReference(“Grasshopper”)
import Grasshopper as gh

def handle_client(client):
try:
stream = client.GetStream()
reader = StreamReader(stream)
writer = StreamWriter(stream)

    # Read JSON command
    json_cmd = reader.ReadLine()
    cmd = parse_command(json_cmd)  # Implement parsing
    
    # Execute on UI thread
    Rhino.RhinoApp.InvokeOnUiThread(lambda: process_command(cmd))
    
    # Send response
    response = get_response()  # Capture outputs
    writer.WriteLine(response)
    writer.Flush()
except Exception as e:
    print(f"Error: {e}")
finally:
    client.Close()

def start_server():
listener = TcpListener(System.Net.IPAddress.Parse(“127.0.0.1”), 12345)
listener.Start()
print(“Server started…”)

while True:
    client = listener.AcceptTcpClient()
    thread = Thread(ThreadStart(lambda: handle_client(client)))
    thread.Start()

Start server in a background thread

server_thread = Thread(ThreadStart(start_server))
server_thread.IsBackground = True
server_thread.Start()
2. Command Processing
python
Copy
def process_command(cmd):
doc = gh.Instances.ActiveCanvas.Document # Access Grasshopper doc
component = next((c for c in doc.Objects if c.NickName == cmd[“component”]), None)

if component:
    # Set inputs (example: first input)
    component.Params.Input[0].AddPersistentData(cmd["inputs"])
    doc.NewSolution(False)  # Recompute
    
    # Capture outputs
    outputs = [str(param.VolatileData[0]) for param in component.Params.Output]
    return {"outputs": outputs}
else:
    return {"error": "Component not found"}
  1. AI Client (Python Example)
    python
    Copy
    import socket
    import json

def send_command(cmd):
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.connect((“localhost”, 12345))
s.sendall(json.dumps(cmd).encode() + b’\n’)
response = s.recv(1024).decode()
return json.loads(response)

Example usage

response = send_command({
“component”: “MyComponent”,
“inputs”: [1.5, 3.7]
})
print(response)
4. Setup & Execution
In Rhino:

Open Grasshopper and load your .gh file.

Run the MCP server script (directly in RhinoPython editor).

AI Client:

Execute the client script, sending commands to localhost:12345.

  1. Key Considerations
    Thread Safety: Use InvokeOnUiThread for Grasshopper operations.

Error Handling: Robust error checking in both server and client.

Performance: Optimize Grasshopper definitions for speed.

Security: Add authentication tokens if exposing beyond localhost.

  1. Advanced Enhancements
    Bidirectional Communication: Implement asynchronous updates from Grasshopper to AI.

Multiple Components: Allow batch commands for complex interactions.

Plugin Integration: Wrap the server into a Grasshopper plugin for ease of use.

This setup enables seamless interaction between an AI and Grasshopper, leveraging local TCP communication for real-time control and feedback.

Rod here again
So I suspect a lot of that is just hopeless dreamy hallucination right?