Official description7 sourcesMCP servers8d ago
SPL-BGU/PlanningCopilot
Tool-augmented LLM system for the
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Config template (adjust for your client)
claude mcp add planningcopilot -- npx -y planningcopilot
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 4 ★ · SPL-BGU/PlanningCopilot · read from the repository page · (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list · type: MCP server
- Harvest history
- first seen 2026-10-09 · last seen 2026-10-16 · seen 32× · listed by 7 sources
- Score
- score 703 · 4 ★ · 7 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
SPL-BGU/PlanningCopilot；first catalogued 2026-10-09；last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.Third-party quality signals
Toward PDDL Planning Copilot
以下摘录自该仓库的 README（片段） · 在 GitHub 读完整 README →
Getting Started
We present the Planning Copilot, a chatbot that brings together multiple planning tools and lets users run them using natural language instructions. It’s built on the Model Context Protocol (MCP), which makes it easy for language models to interact with external tools and systems.
The Planning Copilot is modular, so each part can be swapped out, upgraded, or extended without affecting the rest of the system. In the current implementation, Solve uses FastDownward for classical planning and Metric-FF for numeric planning, Verify uses VAL to validate plans, and Execute relies on PDDL_Plus_Parser to simulate and track plan execution.
Dependencies
1. Make sure that Python 3.10 is installed and active (via virtual environment or conda environment).
2. Install the latest version of Ollama to run it locally.
3. Install all project requirements:
Usage
How to use the environment:
1. Update all the paths and settings in the config.py file.
2. Run the LLM chat with:
3. To change the LLM, edit the llm_with_tools.py file.
4. To add new tools, modify the MCP server in solvers_server.py.
Citations
If you find our work interesting or the repo useful, please consider citing this paper:
We present the Planning Copilot, a chatbot that brings together multiple planning tools and lets users run them using natural language instructions. It’s built on the Model Context Protocol (MCP), which makes it easy for language models to interact with external tools and systems.
The Planning Copilot is modular, so each part can be swapped out, upgraded, or extended without affecting the rest of the system. In the current implementation, Solve uses FastDownward for classical planning and Metric-FF for numeric planning, Verify uses VAL to validate plans, and Execute relies on PDDL_Plus_Parser to simulate and track plan execution.
Dependencies
1. Make sure that Python 3.10 is installed and active (via virtual environment or conda environment).
2. Install the latest version of Ollama to run it locally.
3. Install all project requirements:
Usage
How to use the environment:
1. Update all the paths and settings in the config.py file.
2. Run the LLM chat with:
3. To change the LLM, edit the llm_with_tools.py file.
4. To add new tools, modify the MCP server in solvers_server.py.
Citations
If you find our work interesting or the repo useful, please consider citing this paper:
- Source
- awesome · https://github.com/SPL-BGU/PlanningCopilot
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