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MCP Server Implementation

@jalzoubi

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stdio

作者

jalzoubi

MCP Server Implementation について

概要はまだありません

MCP Server Implementation の設定

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このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

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MCP Server Implementation のツール

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ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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).

2

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`.

3

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.

4

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.

5

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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