Model Coupling Platform Server
MCP 服务器实现
姓名:Esteban Nicolas 学生 ID:A20593170
一、已实现的MCP功能
1 数据资源 1.1 HDF5 文件清单
列出目录结构中的模拟 HDF5 文件
参数:
path_pattern(可选文件路径模式)
2 工具 2.1 Slurm 作业提交
模拟向 Slurm 调度程序提交作业
参数:
script_path(必需)、cores(可选,默认值=1)
2.2 CPU 核心报告
报告系统上可用的 CPU 核心数
无需参数
2.3 CSV可视化
从 CSV 文件中绘制两列(默认为前两列)
参数:
csv_path(必需)、column x、column y(均为可选)
II. 设置说明
创建虚拟环境
uv venv -p python3.10 .venv\Scripts\activate # 在 Unix 上:source .venv/bin/activate
安装依赖项
紫外线同步 紫外线锁定
环境配置本项目使用 pyproject.toml 进行依赖管理,主要依赖包括:
快速API
优维康
派丹蒂克
熊猫
Matplotlib
Pytest
Pytest-ascyncio
运行 MCP 服务器
启动服务器 cd src uvicorn server:app --reload
该服务器将在以下位置可用:
API 端点: http://localhost:8000/mcp健康检查: http://localhost:8000/health
三、测试
运行所有测试:
pytest 测试/运行特定的测试文件:
pytest 测试/test_capabilities_plot_vis.py pytest 测试/test_capabilities_hdf5.py pytest 测试/test_capabilities_cpu_core.py pytest 测试/test_capabilities_slurm.py pytest 测试/test_mcp_handler.py
示例请求 2.1 列出可用资源
curl -X POST http://localhost:8000/mcp
-H“内容类型:application/json”
-d '{“jsonrpc”:“2.0”,“method”:“mcp/listResources”,“id”:1}'
2.2 列出 HDF5 文件
curl -X POST http://localhost:8000/mcp
-H“内容类型:application/json”
-d'{“jsonrpc”:“2.0”,“方法”:“mcp / callTool”,“params”:{“工具”:“hdf5_file_listing”,“path_pattern”:“/ data / sim_run_123”},“id”:2}'
2.3 提交 Slurm 作业
curl -X POST http://localhost:8000/mcp
-H“内容类型:application/json”
-d '{“jsonrpc”:“2.0”,“method”:“mcp / callTool”,“params”:{“tool”:“slurm_job_submission”,“script_path”:“/jobs/analysis.sh”,“cores”:4},“id”:3}'
2.4 绘制 CSV 列
curl -X POST http://localhost:8000/mcp
-H“内容类型:application/json”
-d '{“jsonrpc”:“2.0”,“method”:“mcp/callTool”,“params”:{“tool”:“plot_vis_columns”,“csv_path”:“data.csv”,“column x”:“时间”,“column y”:“温度”},“id”:4}'
四、实施说明
模拟实现:
-HDF5 文件列表使用模拟目录结构 -Slurm 作业提交生成模拟作业 ID -CPU 核心报告使用 os.cpu_count()
CSV 可视化:
在 plots_results 目录中创建图表 - 如果未指定,则默认为前两列 - 返回生成的 PNG 文件的路径
错误处理:
-正确的 JSON-RPC 2.0 错误响应 -所有参数的输入验证 -优雅地处理丢失的文件/无效路径
GITHUB: https://github.com/EstebanIIT/cs550\ _MCP.git
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