pfc-mcp

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MCP server that gives AI agents full access to ITASCA PFC - browse documentation, run simulations, capture plots, all through natural conversation.

Built on the Model Context Protocol, pfc-mcp turns any MCP-compatible AI client (Claude Code, Codex CLI, Gemini CLI, OpenCode, toyoura-nagisa, etc.) into a PFC co-pilot that can look up commands, execute scripts, monitor long-running simulations, and capture visualizations.

<a href="https://glama.ai/mcp/servers/yusong652/pfc-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/yusong652/pfc-mcp/badge" alt="pfc-mcp MCP server" /> </a>

Tools (10)

Documentation (5) - no bridge required

• Browse PFC command tree, Python SDK reference, and reference docs (contact models, range elements)
• Search commands and Python APIs by keyword (BM25 ranked)

Execution (5) - requires bridge in a running PFC process

• Submit Python scripts and poll status/output in real time
• List and manage tasks across sessions
• Interrupt running simulations
• Capture PFC plot images with configurable camera, coloring, and cut planes

Quick Start

Prerequisites

• ITASCA PFC 7.0 installed (pfc2d700_gui.exe or pfc3d700_gui.exe)
• uv installed (for uvx)

Agentic Setup (Recommended)

Copy this to your AI agent and let it self-configure:

Fetch and follow this bootstrap guide end-to-end:
https://raw.githubusercontent.com/yusong652/pfc-mcp/main/docs/agentic/pfc-mcp-bootstrap.md

Manual Setup

1. Register the MCP server in your client config:

{
  "mcpServers": {
    "pfc-mcp": {
      "command": "uvx",
      "args": ["pfc-mcp"]
    }
  }
}

2. Install dependency:

import pip
pip.main(["install", "--user", "-U", "pfc-mcp-bridge"])

Start Bridge & Verify

import pfc_mcp_bridge
pfc_mcp_bridge.start()

Verify - reconnect your MCP client and ask the agent to call pfc_list_tasks to verify the full MCP + bridge connection.

Design Highlights

• Documentation as a boundary map - browse and search tools let agents discover what PFC can do, reducing hallucinated commands
• Task queue with live status - scripts are queued and executed sequentially; agents can poll output and status in real time
• Callback-based control - gracefully interrupt long-running cycle() calls, and capture plots mid-simulation without pausing it

Runtime Model

Component	PyPI	Python	Role
pfc-mcp		>= 3.10	MCP server (documentation + execution client)
pfc-mcp-bridge		>= 3.6	WebSocket bridge inside PFC process (GUI or console)

Documentation tools work standalone. Execution tools require a running bridge.

Troubleshooting

Symptom	Fix
uvx not found	Install uv or switch client MCP config to command: "uv" with args: ["tool", "run", "pfc-mcp"]
Bridge won't start	In PFC Python/IPython console, install/upgrade pfc-mcp-bridge with import pip; pip.main(["install", "--user", "-U", "pfc-mcp-bridge"])
Tasks not processing / cannot connect	If execution tools return ok=false, error.code=bridge_unavailable, and error.details.reason=cannot connect to bridge service, start bridge in PFC (pfc_mcp_bridge.start()) and ensure PFC_MCP_BRIDGE_URL matches the active bridge URL
pfc_capture_plot unsupported	Plot capture requires PFC GUI; console mode does not support it
Bridge on custom port	Set MCP server env PFC_MCP_BRIDGE_URL=ws://localhost:<bridge-port> (for example ws://localhost:9002)
Connection failed	Check bridge is running, target port is available, see .pfc-bridge/bridge.log

Development

uv sync --group dev    # Install with dev dependencies
uv run pytest          # Run tests
uv run pfc-mcp         # Run server locally

License

MIT - see LICENSE.

<!-- mcp-name: io.github.yusong652/pfc -->