Frequently Asked Questions (FAQ) about Esteban Nicolas’ MCP Server
Q: What is an MCP Server?
A: MCP (Model Context Protocol) Server acts as a bridge, allowing AI models to access and interact with external data sources and tools. It provides a standardized way for applications to give context to LLMs (Large Language Models).
Q: What are the key capabilities of this MCP Server implementation?
A: This MCP server implementation includes capabilities such as HDF5 file listing, Slurm job submission (simulated), CPU core reporting, and CSV visualization.
Q: What is HDF5 file listing used for?
A: HDF5 (Hierarchical Data Format version 5) file listing is used for AI applications to access and list HDF5 files within a directory structure, beneficial for scientific data and simulations.
Q: How does the Slurm job submission work in this server?
A: The Slurm job submission simulates job submissions to a Slurm scheduler. You can submit jobs with specified script paths and CPU core allocations.
Q: What kind of data visualization does the CSV visualization offer?
A: The CSV visualization plots two columns from a CSV file, providing a visual representation of data relationships that LLMs can use.
Q: What are the prerequisites for setting up this MCP Server?
A: You need Python 3.10, uv (or pip for virtual environment and dependency management), FastAPI, Uvicorn, Pydantic, Pandas, Matplotlib, Pytest, and Pytest-asyncio.
Q: How do I run the MCP Server after setting it up?
A: Navigate to the ‘src’ directory and run uvicorn server:app --reload.
Q: What are the API and Health check endpoints for this server?
A: The API endpoint is http://localhost:8000/mcp, and the health check is http://localhost:8000/health.
Q: How can I test the MCP Server?
A: Use pytest to run tests in the tests/ directory. Run all tests using the command: pytest tests/.
Q: What are some example requests I can send to the MCP Server?
A: You can list available resources, list HDF5 files, submit Slurm jobs, and plot CSV columns using curl commands. Refer to the documentation for specific examples.
Q: Are the implementations for the data resources and tools mocks?
A: Yes, the HDF5 file listing uses a simulated directory structure, and Slurm job submission generates mock job IDs.
Q: Where are the CSV visualization plots stored?
A: The plots are created in a plots_results directory, and the path to the generated PNG file is returned.
Q: How is error handling implemented in this MCP Server?
A: The server uses proper JSON-RPC 2.0 error responses, includes input validation for parameters, and handles missing files or invalid paths gracefully.
Q: How does this MCP Server implementation integrate with UBOS?
A: Integrating this MCP Server with UBOS enables access to a comprehensive platform with AI agent orchestration, enterprise data connectivity, custom AI agent development, and multi-agent systems.
Q: Where can I find the source code for this MCP Server?
A: The source code is available on GitHub: https://github.com/EstebanIIT/cs550_MCP.git
Model Coupling Platform Server
Project Details
- EstebanIIT/CS550_MCP
- Last Updated: 4/17/2025
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