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