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\n\n# Startup MCP Server 🚀\n1. Clone this repo `git clone https://github.com/nicknochnack/BuildMCPServer`\n2. To run the MCP server\\\n`cd BuildMCPServer`\\\n`uv venv`\\\n`source .venv/bin/activate`\\\n`uv add .`\\\n`uv add \".[dev]\"`\\\n`uv run mcp dev server.py`\n3. To run the agent, in a separate terminal, run:\\\n`source .venv/bin/activate`\\\n`uv run singleflowagent.py`\n\n# Startup FastAPI Hosted ML Server \n`git clone https://github.com/nicknochnack/CodeThat-FastML`\\\n`cd CodeThat-FastML`\\\n`pip install -r requirements.txt`\\\n`uvicorn mlapi:app --reload`\\\nDetailed instructions on how to build it can also be found here
\n\n\n# Other References 🔗
\n- Building MCP Clients (used in singleflow agent)
\n- Original Video where I build the ML server\n\n\n# Who, When, Why?\n👨🏾‍💻 Author: Nick Renotte
\n📅 Version: 1.x
\n📜 License: This project is licensed under the MIT License
","English","tag","en-US",{"_36":48,"_40":52,"_45":53},{"_38":49,"_40":50,"_42":51},"Un servidor que integra modelos de Random Forest entrenados con Bee Framework, lo que permite la interactividad de ReAct para herramientas y agentes de IA.","Servidor MCP para la integración de modelos ML","# Construir un servidor MCP\n\nUn tutorial completo sobre cómo construir un servidor MCP para servir un modelo de Random Forest entrenado e integrarlo con Bee Framework para la interactividad de ReAct.\n\n## Míralo en vivo y en acción 📺\n\n# Servidor MCP de inicio 🚀\n\n1. Clonar este repositorio `git clone https://github.com/nicknochnack/BuildMCPServer`\n2. Para ejecutar el servidor MCP\\\n `cd BuildMCPServer`\\\n `uv venv`\\\n `source .venv/bin/activate`\\\n `uv add .`\\\n `uv add \".[dev]\"`\\\n `uv run mcp dev server.py`\n3. Para ejecutar el agente, en una terminal separada, ejecute:\\\n `source .venv/bin/activate`\\\n `uv run singleflowagent.py`\n\n# Servidor ML alojado de FastAPI para startups\n\n`git clone https://github.com/nicknochnack/CodeThat-FastML`\\\n`cd CodeThat-FastML`\\\n`pip install -r requirements.txt`\\\n`uvicorn mlapi:app --reload`\\\nLas instrucciones detalladas sobre cómo construirlo también se pueden encontrar aquí.\n\n# Otras referencias 🔗\n\n* Creación de clientes MCP (utilizados en el agente de flujo único)\n* Vídeo original donde construyo el servidor ML\n\n# ¿Quién, cuándo, por qué?\n\n👨🏾‍💻 Autor: Nick Renotte 📅 Versión: 1.x 📜 Licencia: Este proyecto está licenciado bajo la Licencia MIT","Spanish","es-ES",{"_36":55,"_40":59,"_45":60},{"_38":56,"_40":57,"_42":58},"トレーニング済みの Random Forest モデルを Bee Framework と統合し、AI ツールとエージェントの ReAct インタラクションを可能にするサーバー。","MLモデル統合のためのMCPサーバー","# MCPサーバーを構築する\n\nトレーニング済みのランダムフォレストモデルを提供するための MCP サーバーを構築し、ReAct のインタラクションのために Bee Framework と統合する方法についての完全なチュートリアル。\n\n## ライブで動作を確認してください📺\n\n# MCP サーバーの起動 🚀\n\n1. このリポジトリをクローンします`git clone https://github.com/nicknochnack/BuildMCPServer`\n2. MCPサーバーを実行するには\\\n `cd BuildMCPServer`\\\n `uv venv`\\\n `source .venv/bin/activate`\\\n `uv add .`\\\n `uv add \".[dev]\"`\\\n `uv run mcp dev server.py`\n3. エージェントを実行するには、別のターミナルで次のコマンドを実行します。\\\n `source .venv/bin/activate`\\\n `uv run singleflowagent.py`\n\n# FastAPIホスト型MLサーバーの起動\n\n`git clone https://github.com/nicknochnack/CodeThat-FastML`\\\n`cd CodeThat-FastML`\\\n`pip install -r requirements.txt`\\\n`uvicorn mlapi:app --reload`\\\n作り方の詳しい説明はここでもご覧いただけます\n\n# その他の参考資料 🔗\n\n* MCP クライアントの構築 (シングルフローエージェントで使用)\n* MLサーバーを構築するオリジナルビデオ\n\n# 誰が、いつ、なぜ？\n\n👨🏾‍💻 作者: Nick Renotte 📅 バージョン: 1.x 📜 ライセンス: このプロジェクトは MIT ライセンスの下でライセンスされています","Japanese","ja-JP",{"_36":62,"_40":66,"_45":67},{"_38":63,"_40":64,"_42":65},"훈련된 랜덤 포레스트 모델을 Bee Framework와 통합하여 AI 도구와 에이전트에 대한 ReAct 상호 작용을 지원하는 서버입니다.","ML 모델 통합을 위한 MCP 서버","# MCP 서버 구축\n\n훈련된 랜덤 포레스트 모델을 제공하고 ReAct 상호 작용을 위해 Bee 프레임워크와 통합하는 MCP 서버를 구축하는 방법에 대한 전체적인 연습입니다.\n\n## 라이브로 직접 확인해보세요 📺\n\n# MCP 서버 시작 🚀\n\n1. 이 저장소를 복제하세요 `git clone https://github.com/nicknochnack/BuildMCPServer`\n2. MCP 서버를 실행하려면\\\n `cd BuildMCPServer`\\\n `uv venv`\\\n `source .venv/bin/activate`\\\n `uv add .`\\\n `uv add \".[dev]\"`\\\n `uv run mcp dev server.py`\n3. 에이전트를 실행하려면 별도의 터미널에서 다음을 실행하세요.\\\n `source .venv/bin/activate`\\\n `uv run singleflowagent.py`\n\n# FastAPI 호스팅 ML 서버 시작\n\n`git clone https://github.com/nicknochnack/CodeThat-FastML`\\\n`cd CodeThat-FastML`\\\n`pip install -r requirements.txt`\\\n`uvicorn mlapi:app --reload`\\\n자세한 제작 방법은 여기에서도 확인할 수 있습니다.\n\n# 기타 참고 자료 🔗\n\n* MCP 클라이언트 구축(SingleFlow 에이전트에서 사용)\n* ML 서버를 구축하는 원본 비디오\n\n# 누가, 언제, 왜?\n\n👨🏾‍💻 저자: Nick Renotte 📅 버전: 1.x 📜 라이선스: 이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다.","Korean","ko-KR",{"_36":69,"_40":73,"_45":74},{"_38":70,"_40":71,"_42":72},"将经过训练的随机森林模型与 Bee Framework 相集成的服务器，使 AI 工具和代理能够与 ReAct 进行交互。","用于 ML 模型集成的 MCP 服务器","# 构建 MCP 服务器\n\n有关如何构建 MCP 服务器以服务于经过训练的随机森林模型并将其与 Bee Framework 集成以实现 ReAct 交互的完整演练。\n\n## 亲眼见证它的精彩表现📺\n\n# 启动 MCP 服务器🚀\n\n1. 克隆此 repo `git clone https://github.com/nicknochnack/BuildMCPServer`\n2. 运行 MCP 服务器\\\n `cd BuildMCPServer`\\\n `uv venv`\\\n `source .venv/bin/activate`\\\n `uv add .`\\\n `uv add \".[dev]\"`\\\n `uv run mcp dev server.py`\n3. 要运行代理，请在单独的终端中运行：\\\n `source .venv/bin/activate`\\\n `uv run singleflowagent.py`\n\n# 启动 FastAPI 托管的 ML 服务器\n\n`git clone https://github.com/nicknochnack/CodeThat-FastML`\\\n`cd CodeThat-FastML`\\\n`pip install -r requirements.txt`\\\n`uvicorn mlapi:app --reload`\\\n关于如何构建它的详细说明也可以在这里找到\n\n# 其他参考\n\n* 构建 MCP 客户端（用于单流代理）\n* 我构建机器学习服务器的原始视频\n\n# 谁、何时、为什么？\n\n👨🏾‍💻 作者：Nick Renotte 📅 版本：1.x 📜 许可证：本项目采用 MIT 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