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Exposes a trained Deep Q-Network agent for business process resource allocation, enabling natural language interaction with reinforcement learning models. It pr
Exposes a trained Deep Q-Network agent for business process resource allocation, enabling natural language interaction with reinforcement learning models. It provides tools for simulation control, Q-value analysis, and action explainability to make complex decision-making transparent.
MCP server that exposes a trained Deep Q-Network (DQN) agent for business process resource allocation through conversational interfaces. Makes "black box" RL systems transparent via natural language queries.
pip install -r requirements.txt
Requirements: Python 3.8+, TensorFlow 2.16+
python -m mcp4drl.test_integration
# Windows
run_server.bat
# Linux/Mac
chmod +x run_server.sh
./run_server.sh
Add to claude_desktop_config.json:
{
"mcpServers": {
"mcp4drl": {
"command": "cmd.exe",
"args": ["/c", "C:\\path\\to\\mcp4drl_repo\\run_server.bat"],
"shell": true
}
}
}
| Tool | Description |
|---|---|
get_environment_state |
Current simulation state |
get_eligible_actions |
All possible actions with validity |
get_q_values |
Q-values for all actions |
get_recommended_action |
Agent's best action |
explain_action |
Detailed action explanation |
compare_with_heuristic |
Compare with FIFO/SPT/EDF/LST |
step_simulation |
Execute one step |
reset_simulation |
Reset to initial state |
run_episode |
Run full episode with policy |
mcp4drl_repo/
├── mcp4drl/ # Main Python package
│ ├── core/ # Wrappers (simulator, agent)
│ ├── models/ # Pydantic schemas
│ └── tools/ # MCP tool implementations
├── simprocess/ # Business process simulation engine
├── data/ # Model and event log
└── mcp4drl_server.py # Standalone launcher
Environment variables (optional):
MCP4DRL_MODEL_PATH - Path to trained model (.h5)MCP4DRL_EVENT_LOG - Path to XES event logMCP4DRL_TRANSPORT - stdio (default) or ssePart of doctoral dissertation on intelligent automation of business process management. Demonstrates that RL systems can be made transparent through conversational interfaces.
Research prototype.
Run in your terminal:
claude mcp add mcp4drl -- npx CSA PROJECT - FZCO © 2026 IFZA Business Park, DDP, Premises Number 31174 - 001
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