官方说明7 个来源MCP 服务8 天前
SPL-BGU/PlanningCopilot
Tool-augmented LLM system for the
在 GitHub 查看 ↗直链 · 无中转页
配置模板（请按你的客户端调整）
claude mcp add planningcopilot -- npx -y planningcopilot
这是根据仓库名生成的可粘贴骨架，命令行参数与包名请以该项目 README 为准。
- ★ 数取值
- 4 ★ · SPL-BGU/PlanningCopilot · 取自该仓库的 GitHub 页面 · （2026-10-16 20:00:20 核对）
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单（我们从中收集的条目之一） · 条目类型：MCP 服务
- 收录与活跃
- 收录于 2026-10-09 · 最近见到 2026-10-16 · 共见到 32 次 · 被 7 个来源列出
- 评分构成
- 综合分 703 · 4 ★ · 7 个来源 · mcp
常见问题（答案都来自我们的记录）
怎么安装？
我们【没有】从该仓库取到安装命令（我们的 install 覆盖率只有约 4%）。请看它的 README 或 releases 页。
它适合谁 / 用来做什么？
我们把它归在这些标签下: MCP 服务。标签来自条目自身，不是我们强加的分类。
谁维护它、还在更新吗？
仓库
SPL-BGU/PlanningCopilot；我们收录于 2026-10-09；最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。第三方质量标记
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:
- 来源
- awesome · https://github.com/SPL-BGU/PlanningCopilot
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- MCP 服务
- Traction
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