MCP Interface¶

AutoMOOSE exposes a Model Context Protocol (MCP) server that allows external LLM agents and tools to interact with the simulation pipeline programmatically.

Server Details¶

Transport: stdio and SSE
Host: 0.0.0.0
Port: 8001
Framework: Starlette / uvicorn

The MCP server acts as a hub, routing tool calls from external clients to the five-agent pipeline and plugin registry.

Available Tools¶

The server exposes ten tools:

Tool Name	Description
`list_plugins`	List all registered physics plugins and their status
`create_run`	Initialize a new simulation run with given parameters
`generate_input`	Invoke f₂ Input Writer to produce a MOOSE `.i` file
`submit_run`	Execute the simulation via f₃ Runner
`get_run_status`	Poll run status from `record.json`
`get_run_log`	Retrieve `run.log` content for a given run
`review_run`	Invoke f₄ Reviewer on completed run output
`get_results`	Return parsed postprocessor CSV data
`visualize_run`	Trigger f₅ Visualization for a completed run
`list_runs`	List all runs with status and metadata

Example: Creating a Run via MCP¶

import httpx

response = httpx.post("http://localhost:8001/mcp/tool", json={
    "tool": "create_run",
    "params": {
        "plugin": "GrainGrowth",
        "temperature": 600,
        "duration": 100,
        "mesh_size": [50, 50]
    }
})
print(response.json())

Future Directions¶

RAG (Retrieval-Augmented Generation) integration is planned as a future extension to augment the Architect agent with retrieval over MOOSE documentation and prior simulation records.