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MCP Hub Documentation

@reddy-sh

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MCP Hub Documentation について

MCP Hub is a comprehensive framework for building, managing, and deploying Model Context Protocol (MCP) clients and servers. It provides tools and configurations to enable seamless integration and execution of end-to-end MCP workflows.

#python#mcp#llm#llmapps#mcp-server#modelcontextprotocol#mcp-client

基本情報

カテゴリ

メモリとナレッジ

ライセンス

Apache-2.0

ランタイム

python

トランスポート

stdio

公開者

reddy-sh

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-hub-reddy-sh": {
      "command": "uv",
      "args": [
        "init"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

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 Hub Documentation?

MCP Hub Documentation is a framework for creating and managing Model Context Protocol (MCP) servers and clients. It leverages the uv tool for fast package installation and configuration management.

How to use MCP Hub Documentation?

Initialize a project with uv init, set up a virtual environment with uv venv, activate it, then install dependencies using uv add "mcp[cli]" httpx. Create a server file (e.g., XYZ.py) and run it with uv run XYZ.py.

Key features of MCP Hub Documentation

  • Uses uv for fast package and configuration management.
  • Provides step‑by‑step instructions to create an MCP server.
  • Includes sample scripts for image processing, machine learning, and computer vision.
  • Contains JupyterHub configuration and startup scripts.
  • Offers a script to download the CIFAR‑10 dataset.
  • Supports virtual environment setup and dependency locking.

Use cases of MCP Hub Documentation

  • Creating a new MCP server from scratch.
  • Running an MCP server for local development or testing.
  • Leveraging computer‑vision and AI scripts for learning or prototyping.
  • Setting up a JupyterHub environment for collaborative notebooks.

FAQ from MCP Hub Documentation

What is the purpose of MCP Hub Documentation?

It is a framework to create and manage MCP servers and clients, providing tools and example code.

Which dependencies are required?

You need uv, mcp[cli], and httpx. The README also references Python and a virtual environment.

How do I run an MCP server?

After creating the server file, run uv run XYZ.py, where XYZ is your project name.

What is the role of the ai/ folder?

It contains subdirectories with scripts for image handling, preprocessing, feature extraction, ML, deep learning, classification, object detection, segmentation, and dataset utilities.

Are there any known limitations or transport details?

—

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