Question 1

What is an MCP Server?

Accepted Answer

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).

Question 2

What are the key capabilities of this MCP Server implementation?

Accepted Answer

This MCP server implementation includes capabilities such as HDF5 file listing, Slurm job submission (simulated), CPU core reporting, and CSV visualization.

Question 3

What is HDF5 file listing used for?

Accepted Answer

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.

Question 4

How does the Slurm job submission work in this server?

Accepted Answer

The Slurm job submission simulates job submissions to a Slurm scheduler. You can submit jobs with specified script paths and CPU core allocations.

Question 5

What kind of data visualization does the CSV visualization offer?

Accepted Answer

The CSV visualization plots two columns from a CSV file, providing a visual representation of data relationships that LLMs can use.

Question 6

What are the prerequisites for setting up this MCP Server?

Accepted Answer

You need Python 3.10, uv (or pip for virtual environment and dependency management), FastAPI, Uvicorn, Pydantic, Pandas, Matplotlib, Pytest, and Pytest-asyncio.

Question 7

How do I run the MCP Server after setting it up?

Accepted Answer

Navigate to the 'src' directory and run `uvicorn server:app --reload`.

Question 8

What are the API and Health check endpoints for this server?

Accepted Answer

The API endpoint is `http://localhost:8000/mcp`, and the health check is `http://localhost:8000/health`.

Question 9

How can I test the MCP Server?

Accepted Answer

Use `pytest` to run tests in the `tests/` directory. Run all tests using the command: `pytest tests/`.

Question 10

What are some example requests I can send to the MCP Server?

Accepted Answer

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.

Question 11

Are the implementations for the data resources and tools mocks?

Accepted Answer

Yes, the HDF5 file listing uses a simulated directory structure, and Slurm job submission generates mock job IDs.

Question 12

Where are the CSV visualization plots stored?

Accepted Answer

The plots are created in a `plots_results` directory, and the path to the generated PNG file is returned.

Question 13

How is error handling implemented in this MCP Server?

Accepted Answer

The server uses proper JSON-RPC 2.0 error responses, includes input validation for parameters, and handles missing files or invalid paths gracefully.

Question 14

How does this MCP Server implementation integrate with UBOS?

Accepted Answer

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.

Question 15

Where can I find the source code for this MCP Server?

Accepted Answer

The source code is available on GitHub: [https://github.com/EstebanIIT/cs550_MCP.git](https://github.com/EstebanIIT/cs550_MCP.git)

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