MCP Server Implementation

@jalzoubi

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Basic information

About MCP Server Implementation

No overview available yet

MCP Server Implementation config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

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Overview

What is MCP Server Implementation?

MCP Server Implementation is an MCP server that provides two capabilities: HDF5 data operations (list, read, metadata) and Slurm job simulation (submit, track status). It is intended for developers needing HDF5 data access and Slurm-like scheduling in a mock/local environment, using JSON-RPC 2.0 communication.

How to use MCP Server Implementation?

Install uv, create and activate a virtual environment, sync dependencies with uv sync, then start the server with uvicorn src.server:app --reload. Interact via HTTP POST to http://127.0.0.1:8000/mcp with JSON-RPC 2.0 payloads. Run tests with pytest tests/.

Key features of MCP Server Implementation

HDF5 data operations (list, read, metadata)
Slurm job submission simulation with status tracking
Full JSON-RPC 2.0 compliance
Async request processing
100% test coverage for both capabilities
Mock data included in mock_data/ directory

Use cases of MCP Server Implementation

Testing HDF5 data pipelines without real HDF5 files
Simulating Slurm job scheduling on a local machine
Developing and testing MCP tools that interact with HDF5 or Slurm
Learning HDF5 and Slurm APIs in a safe, controlled environment

Frequently asked questions

What capabilities does the server provide?

The server provides HDF5 data operations (list directory, read datasets, retrieve metadata) and Slurm job submission (submit scripts, track job status in memory).

How do I set up and run the server?

Install uv, run `uv venv` to create a virtual environment, activate it, then `uv sync` to install dependencies. Finally, start with `uvicorn src.server:app --reload`. Mock data must reside in `mock_data/hdf5` and `mock_data/slurm`.

What are the assumptions and limitations?

HDF5 mock data is stored locally in `mock_data/hdf5`. Slurm simulation uses local echo commands and in-memory job state tracking; it does not connect to a real Slurm cluster.

What transport and authentication does the server use?

The server communicates over HTTP on `127.0.0.1:8000` using JSON-RPC 2.0. No authentication mechanism is documented.

How do I run tests and check coverage?

Use `pytest tests/` to run all tests. For specific capabilities, run `pytest tests/test_hdf5.py -v` or `pytest tests/test_slurm.py -v`. Generate a coverage report with `pytest --cov=src`.

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