[{"_1":2,"_335":-5,"_336":-5},"loaderData",{"_3":4,"_29":30,"_245":246},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","0199a162-69f6-77ad-8b18-e555fe7b63d7","title","This MCP server does not support inspection","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"bot","id",17365307,"membership","referenceId","0199a0f5-80f3-75d9-86a5-0e41e3be6810","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_31":32,"_73":-5,"_74":33,"_34":75,"_162":163,"_243":244},"availableLocales",[33,45,52,59,66],{"_34":35,"_38":42,"_43":44},"mcpServer",{"_36":37,"_38":39,"_40":41},"description","A Model Context Protocol server that enhances LLM capabilities by connecting to Wikipedia, internet search (Tavily), and financial data (Yahoo Finance) tools, enabling contextual responses to user queries.","name","Agentic AI with MCP","readme","# Agentic AI with Model Context Protocol (MCP)\n\nThis project implements an Agentic AI system that connects a Groq-hosted LLM (qwen-qwq-32b model) with various tools through a custom Model Context Protocol (MCP) server. The system enhances the LLM's capabilities by providing contextual information from Wikipedia, internet search (via Tavily API), and financial data (via Yahoo Finance API).\n\n## About MCP\n\nThe Model Context Protocol (MCP) is an open standard developed by Anthropic to standardize how applications provide context to large language models (LLMs). It facilitates seamless integration between LLM applications and external data sources and tools, allowing AI systems to interact dynamically with various services through a standardized interface. \n\nKey Features of MCP:\n* Standardization: Provides a universal protocol for interfacing AI assistants with structured tools and data layers.\n* Modular Architecture: Follows a client–server pattern over a persistent stream, typically mediated by a host AI system.\n* Dynamic Introspection: Supports dynamic discovery of tools and resources through methods like tools/list and resources/list.\n\nSecurity: Incorporates host-mediated authentication and supports secure transport protocols.\nBy adopting MCP, developers can build AI applications that are more interoperable, secure, and capable of complex workflows.\n\nTo add new tools to the MCP server:\n* Define the Tool: Create a new function that handles the specific task or data retrieval.\n* Register the Tool: Update the server's tool registry to include the new function, specifying the tool's name and description.\n* Handle Requests: Ensure the server can route incoming requests to the appropriate tool based on the query.\nThis modular approach allows for easy expansion of the server's capabilities, enabling the language model to access a broader range of contextual information.\n\n## Features\n\n- **MCP Server**: Central hub that provides access to various tools\n- **Three Integrated Tools**:\n 1. Wikipedia Search - for factual information retrieval\n 2. Internet Search - powered by Tavily API for comprehensive web results\n 3. Yahoo Finance API - for real-time stock and financial data\n- **Groq API Integration**: Ultra-fast LLM processing using qwen-qwq-32b model\n- **Client-Server Architecture**: Clean separation between tool management and LLM interaction\n\n## Prerequisites\n\nBefore you begin, ensure you have the following:\n\n- Install UV fro python [installation](https://docs.astral.sh/uv/getting-started/installation/) \n- Groq API key. Refer to the [documentation](https://docs.aicontentlabs.com/articles/groq-api-key/)\n- Tavily API key (sign up at [Tavily AI](https://tavily.com/))\n\n## Installation\n\n1. Clone the repository:\n ```\n git clone https://github.com/dev484p/AgenticAI_MCP\n cd AgenticAI_MCP\n ```\n2. Install dependencies:\n ```\n uv add \"mcp[cli]\"\n ```\n3. Set up your environment variables:\n Update Groq and Tavily api key in keys.json\n \n4. Optional (To run the server with the MCP Inspector for development):\n ```\n uv run mcp dev server.py\n ```\n5. Run the following command to initiate the chatbot:\n ```\n uv run client.py\n ```\n \n## Available Tools\n\nThe system provides three tools through the MCP server:\n1. Wiki Search:\n * Access Wikipedia information\n * Example query: \"Tell me about the history of artificial intelligence\"\n2. Internet Search (Tavily):\n * Get comprehensive web search results\n * Example query: \"What are the latest developments in quantum computing?\"\n3. Yahoo Finance:\n * Access stock prices and financial data\n * Example query: \"What is the current price of AAPL stock?\"\n\n## Refrence\n* https://modelcontextprotocol.io/introduction\n* https://github.com/langchain-ai/langchain-mcp-adapters\n* https://github.com/krishnaik06/MCP-CRASH-Course\n","English","tag","en-US",{"_34":46,"_38":50,"_43":51},{"_36":47,"_38":48,"_40":49},"Un servidor de protocolo de contexto modelo que mejora las capacidades de LLM al conectarse a Wikipedia, herramientas de búsqueda en Internet (Tavily) y datos financieros (Yahoo Finance), lo que permite respuestas contextuales a las consultas de los usuarios.","IA agente con MCP","# IA agente con protocolo de contexto de modelo (MCP)\n\nEste proyecto implementa un sistema de IA Agentic que conecta un LLM alojado en Groq (modelo qwen-qwq-32b) con diversas herramientas mediante un servidor de Protocolo de Contexto de Modelo (MCP) personalizado. El sistema mejora las capacidades del LLM al proporcionar información contextual de Wikipedia, búsquedas en internet (mediante la API de Tavily) y datos financieros (mediante la API de Yahoo Finance).\n\n## Acerca de MCP\n\nEl Protocolo de Contexto de Modelo (MCP) es un estándar abierto desarrollado por Anthropic para estandarizar la forma en que las aplicaciones proporcionan contexto a los grandes modelos lingüísticos (LLM). Facilita la integración fluida entre las aplicaciones LLM y las fuentes de datos y herramientas externas, permitiendo que los sistemas de IA interactúen dinámicamente con diversos servicios a través de una interfaz estandarizada.\n\nCaracterísticas principales de MCP:\n\n* Estandarización: proporciona un protocolo universal para interconectar asistentes de IA con herramientas estructuradas y capas de datos.\n* Arquitectura modular: sigue un patrón cliente-servidor a través de un flujo persistente, generalmente mediado por un sistema de inteligencia artificial host.\n* Introspección dinámica: admite el descubrimiento dinámico de herramientas y recursos a través de métodos como herramientas/lista y recursos/lista.\n\nSeguridad: Incorpora autenticación mediada por el host y admite protocolos de transporte seguro. Al adoptar MCP, los desarrolladores pueden crear aplicaciones de IA más interoperables, seguras y capaces de gestionar flujos de trabajo complejos.\n\nPara agregar nuevas herramientas al servidor MCP:\n\n* Definir la herramienta: crear una nueva función que maneje la tarea específica o la recuperación de datos.\n* Registrar la herramienta: actualice el registro de herramientas del servidor para incluir la nueva función, especificando el nombre y la descripción de la herramienta.\n* Manejar solicitudes: garantizar que el servidor pueda enrutar las solicitudes entrantes a la herramienta adecuada en función de la consulta. Este enfoque modular permite una fácil expansión de las capacidades del servidor, lo que permite que el modelo de lenguaje acceda a una gama más amplia de información contextual.\n\n## Características\n\n* **Servidor MCP** : Centro central que proporciona acceso a varias herramientas\n* **Tres herramientas integradas** :\n 1. Búsqueda en Wikipedia: para recuperar información factual\n 2. Búsqueda en Internet: con tecnología de API de Tavily para obtener resultados web completos\n 3. API de Yahoo Finance: para datos bursátiles y financieros en tiempo real\n* **Integración de la API de Groq** : procesamiento LLM ultrarrápido con el modelo qwen-qwq-32b\n* **Arquitectura cliente-servidor** : separación clara entre la gestión de herramientas y la interacción LLM\n\n## Prerrequisitos\n\nAntes de comenzar, asegúrese de tener lo siguiente:\n\n* Instalar UV para [la instalación de](https://docs.astral.sh/uv/getting-started/installation/) Python\n* Clave API de Groq. Consulta la [documentación.](https://docs.aicontentlabs.com/articles/groq-api-key/)\n* Clave API de Tavily (regístrese en [Tavily AI](https://tavily.com/) )\n\n## Instalación\n\n1. Clonar el repositorio:\n\n ```\n git clone https://github.com/dev484p/AgenticAI_MCP\n cd AgenticAI_MCP\n ```\n\n2. Instalar dependencias:\n\n ```\n uv add \"mcp[cli]\"\n ```\n\n3. Configura tus variables de entorno: Actualiza la clave API de Groq y Tavily en keys.json\n\n4. Opcional (para ejecutar el servidor con el Inspector MCP para desarrollo):\n\n```\nuv run mcp dev server.py\n```\n\n5. Ejecute el siguiente comando para iniciar el chatbot:\n\n ```\n uv run client.py\n ```\n\n## Herramientas disponibles\n\nEl sistema proporciona tres herramientas a través del servidor MCP:\n\n1. Búsqueda en Wiki:\n\n* Acceder a la información de Wikipedia\n* Ejemplo de consulta: \"Cuéntame sobre la historia de la inteligencia artificial\"\n\n2. Búsqueda en Internet (Tavily):\n\n* Obtenga resultados de búsqueda web completos\n* Ejemplo de consulta: \"¿Cuáles son los últimos avances en computación cuántica?\"\n\n3. Yahoo Finanzas:\n\n* Acceda a precios de acciones y datos financieros\n* Ejemplo de consulta: \"¿Cuál es el precio actual de las acciones de AAPL?\"\n\n## Referencia\n\n* \n* \n* ","Spanish","es-ES",{"_34":53,"_38":57,"_43":58},{"_36":54,"_38":55,"_40":56},"Wikipedia、インターネット検索 (Tavily)、金融データ (Yahoo Finance) ツールに接続して LLM 機能を強化し、ユーザークエリに対するコンテキスト応答を可能にするモデルコンテキストプロトコルサーバー。","MCP によるエージェント型 AI","# モデルコンテキストプロトコル（MCP）を備えたエージェント型AI\n\nこのプロジェクトは、Groqホスト型LLM（qwen-qwq-32bモデル）をカスタムモデルコンテキストプロトコル（MCP）サーバーを介して様々なツールに接続するエージェント型AIシステムを実装します。このシステムは、Wikipedia、インターネット検索（Tavily API経由）、金融データ（Yahoo Finance API経由）からのコンテキスト情報を提供することで、LLMの機能を強化します。\n\n## MCPについて\n\nモデルコンテキストプロトコル（MCP）は、Anthropicが開発したオープンスタンダードであり、アプリケーションが大規模言語モデル（LLM）にコンテキストを提供する方法を標準化します。MCPは、LLMアプリケーションと外部データソースおよびツールとのシームレスな統合を促進し、AIシステムが標準化されたインターフェースを介して様々なサービスと動的に連携することを可能にします。\n\nMCP の主な機能:\n\n* 標準化: AI アシスタントを構造化されたツールおよびデータレイヤーとインターフェイスするための汎用プロトコルを提供します。\n* モジュラーアーキテクチャ: 通常はホスト AI システムによって仲介される永続ストリーム上のクライアントサーバーパターンに従います。\n* 動的イントロスペクション: tools/list や resources/list などのメソッドを通じてツールとリソースの動的な検出をサポートします。\n\nセキュリティ: ホストを介した認証を組み込み、安全なトランスポートプロトコルをサポートします。MCP を採用することで、開発者は相互運用性とセキュリティが向上し、複雑なワークフローに対応できる AI アプリケーションを構築できます。\n\nMCP サーバーに新しいツールを追加するには:\n\n* ツールを定義する: 特定のタスクまたはデータの取得を処理する新しい関数を作成します。\n* ツールを登録する: ツールの名前と説明を指定して、新しい機能が含まれるようにサーバーのツールレジストリを更新します。\n* リクエストの処理: サーバーがクエリに基づいて受信リクエストを適切なツールにルーティングできるようにします。このモジュール式のアプローチにより、サーバーの機能を簡単に拡張でき、言語モデルがより広範なコンテキスト情報にアクセスできるようになります。\n\n## 特徴\n\n* **MCP サーバー**: さまざまなツールへのアクセスを提供する中央ハブ\n* **3つの統合ツール**:\n 1. Wikipedia検索 - 事実情報の検索\n 2. インターネット検索 - Tavily API を活用した包括的なウェブ検索結果\n 3. Yahoo Finance API - リアルタイムの株価と金融データ\n* **Groq API 統合**: qwen-qwq-32b モデルを使用した超高速 LLM 処理\n* **クライアントサーバーアーキテクチャ**: ツール管理とLLMの相互作用を明確に分離\n\n## 前提条件\n\n始める前に、次のものがあることを確認してください。\n\n* Python[インストール](https://docs.astral.sh/uv/getting-started/installation/)からUVをインストールする\n* Groq APIキー。[ドキュメント](https://docs.aicontentlabs.com/articles/groq-api-key/)を参照してください。\n* Tavily APIキー（ [Tavily AI](https://tavily.com/)にサインアップ）\n\n## インストール\n\n1. リポジトリをクローンします。\n\n ```\n git clone https://github.com/dev484p/AgenticAI_MCP\n cd AgenticAI_MCP\n ```\n\n2. 依存関係をインストールします:\n\n ```\n uv add \"mcp[cli]\"\n ```\n\n3. 環境変数を設定します:keys.json の Groq および Tavily API キーを更新します\n\n4. オプション (開発用に MCP Inspector を使用してサーバーを実行する場合):\n\n```\nuv run mcp dev server.py\n```\n\n5. チャットボットを開始するには、次のコマンドを実行します。\n\n ```\n uv run client.py\n ```\n\n## 利用可能なツール\n\nシステムは、MCP サーバーを通じて次の 3 つのツールを提供します。\n\n1. ウィキ検索:\n\n* Wikipediaの情報にアクセスする\n* クエリ例:「人工知能の歴史について教えてください」\n\n2. インターネット検索（Tavily）：\n\n* 包括的なウェブ検索結果を取得する\n* クエリの例: 「量子コンピューティングの最新の開発状況は何ですか?」\n\n3. ヤフーファイナンス:\n\n* 株価や財務データにアクセスする\n* クエリの例: 「AAPL 株の現在の価格はいくらですか?」\n\n## 参照\n\n* \n* \n* ","Japanese","ja-JP",{"_34":60,"_38":64,"_43":65},{"_36":61,"_38":62,"_40":63},"Wikipedia, 인터넷 검색(Tavily), 재무 데이터(Yahoo Finance) 도구에 연결하여 사용자 쿼리에 대한 상황에 맞는 응답을 가능하게 함으로써 LLM 기능을 강화하는 모델 컨텍스트 프로토콜 서버입니다.","MCP를 사용한 에이전트 AI","# 모델 컨텍스트 프로토콜(MCP)을 갖춘 에이전트 AI\n\n이 프로젝트는 Groq 기반 LLM(qwen-qwq-32b 모델)을 맞춤형 모델 컨텍스트 프로토콜(MCP) 서버를 통해 다양한 도구와 연결하는 Agentic AI 시스템을 구현합니다. 이 시스템은 위키피디아, 인터넷 검색(Tavily API를 통해), 재무 데이터(Yahoo Finance API를 통해)의 맥락 정보를 제공하여 LLM의 기능을 향상시킵니다.\n\n## MCP 소개\n\n모델 컨텍스트 프로토콜(MCP)은 Anthropic에서 개발한 개방형 표준으로, 애플리케이션이 대규모 언어 모델(LLM)에 컨텍스트를 제공하는 방식을 표준화하기 위해 개발되었습니다. LLM 애플리케이션과 외부 데이터 소스 및 도구 간의 원활한 통합을 지원하여 AI 시스템이 표준화된 인터페이스를 통해 다양한 서비스와 동적으로 상호 작용할 수 있도록 합니다.\n\nMCP의 주요 특징:\n\n* 표준화: AI 도우미와 구조화된 도구 및 데이터 계층을 연결하기 위한 범용 프로토콜을 제공합니다.\n* 모듈식 아키텍처: 일반적으로 호스트 AI 시스템을 통해 중재되는 지속적인 스트림에 대한 클라이언트-서버 패턴을 따릅니다.\n* 동적 내성: tools/list 및 resources/list와 같은 메서드를 통해 도구와 리소스의 동적 검색을 지원합니다.\n\n보안: 호스트 매개 인증을 통합하고 보안 전송 프로토콜을 지원합니다. MCP를 채택하면 개발자는 상호 운용성, 보안성이 뛰어나고 복잡한 워크플로를 처리할 수 있는 AI 애플리케이션을 구축할 수 있습니다.\n\nMCP 서버에 새 도구를 추가하려면:\n\n* 도구 정의: 특정 작업이나 데이터 검색을 처리하는 새로운 기능을 만듭니다.\n* 도구 등록: 도구의 이름과 설명을 지정하여 서버의 도구 레지스트리를 업데이트하여 새 기능을 포함합니다.\n* 요청 처리: 서버가 쿼리에 따라 들어오는 요청을 적절한 도구로 라우팅할 수 있도록 합니다. 이러한 모듈식 접근 방식을 사용하면 서버 기능을 쉽게 확장할 수 있어 언어 모델이 더 광범위한 컨텍스트 정보에 액세스할 수 있습니다.\n\n## 특징\n\n* **MCP 서버** : 다양한 도구에 대한 액세스를 제공하는 중앙 허브\n* **3가지 통합 도구** :\n 1. 위키피디아 검색 - 사실 정보 검색\n 2. 인터넷 검색 - 포괄적인 웹 결과를 제공하는 Tavily API 기반\n 3. Yahoo Finance API - 실시간 주식 및 재무 데이터\n* **Groq API 통합** : qwen-qwq-32b 모델을 사용한 초고속 LLM 처리\n* **클라이언트-서버 아키텍처** : 도구 관리와 LLM 상호 작용 간의 명확한 분리\n\n## 필수 조건\n\n시작하기 전에 다음 사항이 있는지 확인하세요.\n\n* 파이썬 [설치](https://docs.astral.sh/uv/getting-started/installation/) 에서 UV 설치\n* Groq API 키. [설명서](https://docs.aicontentlabs.com/articles/groq-api-key/) 를 참조하세요.\n* Tavily API 키( [Tavily AI](https://tavily.com/) 에 가입하세요)\n\n## 설치\n\n1. 저장소를 복제합니다.\n\n 지엑스피1\n\n2. 종속성 설치:\n\n ```\n uv add \"mcp[cli]\"\n ```\n\n3. 환경 변수 설정: keys.json에서 Groq 및 Tavily API 키 업데이트\n\n4. 선택 사항(개발을 위해 MCP Inspector로 서버를 실행하려면):\n\n```\nuv run mcp dev server.py\n```\n\n5. 다음 명령을 실행하여 챗봇을 시작하세요.\n\n ```\n uv run client.py\n ```\n\n## 사용 가능한 도구\n\n이 시스템은 MCP 서버를 통해 세 가지 도구를 제공합니다.\n\n1. 위키 검색:\n\n* 위키피디아 정보에 접근하세요\n* 예시 쿼리: \"인공지능의 역사에 대해 알려주세요\"\n\n2. 인터넷 검색(Tavily):\n\n* 포괄적인 웹 검색 결과를 얻으세요\n* 예시 질의: \"양자 컴퓨팅의 최신 개발 사항은 무엇입니까?\"\n\n3. 야후 파이낸스:\n\n* 주가 및 재무 데이터에 접근하세요\n* 예시 쿼리: \"AAPL 주식의 현재 가격은 얼마인가요?\"\n\n## 참고문헌\n\n* \n* \n* ","Korean","ko-KR",{"_34":67,"_38":71,"_43":72},{"_36":68,"_38":69,"_40":70},"模型上下文协议服务器通过连接维基百科、互联网搜索（Tavily）和财务数据（雅虎财经）工具来增强 LLM 功能，从而能够对用户查询进行上下文响应。","带有 MCP 的 Agentic AI","# 具有模型上下文协议（MCP）的代理人工智能\n\n该项目实现了一个 Agentic AI 系统，该系统通过自定义模型上下文协议 (MCP) 服务器将 Groq 托管的 LLM（qwen-qwq-32b 模型）与各种工具连接起来。该系统通过提供来自维基百科、互联网搜索（通过 Tavily API）和金融数据（通过 Yahoo Finance API）的上下文信息来增强 LLM 的功能。\n\n## 关于 MCP\n\n模型上下文协议 (MCP) 是由 Anthropic 开发的一项开放标准，旨在规范应用程序向大型语言模型 (LLM) 提供上下文的方式。它促进了 LLM 应用程序与外部数据源和工具之间的无缝集成，使 AI 系统能够通过标准化接口与各种服务动态交互。\n\nMCP 的主要特点：\n\n* 标准化：为人工智能助手与结构化工具和数据层之间的连接提供通用协议。\n* 模块化架构：遵循持久流上的客户端-服务器模式，通常由主机 AI 系统介导。\n* 动态自省：支持通过 tools/list 和 resources/list 等方法动态发现工具和资源。\n\n安全性：集成主机介导身份验证并支持安全传输协议。通过采用 MCP，开发人员可以构建更具互操作性、安全性更高且能够处理复杂工作流程的 AI 应用程序。\n\n要向 MCP 服务器添加新工具：\n\n* 定义工具：创建一个处理特定任务或数据检索的新功能。\n* 注册工具：更新服务器的工具注册表以包含新功能，指定工具的名称和描述。\n* 处理请求：确保服务器可以根据查询将传入的请求路由到适当的工具。这种模块化方法可以轻松扩展服务器的功能，使语言模型能够访问更广泛的上下文信息。\n\n## 特征\n\n* **MCP 服务器**：提供各种工具访问的中央枢纽\n* **三个集成工具**：\n 1. 维基百科搜索——用于事实信息检索\n 2. 互联网搜索 - 由 Tavily API 提供支持，可提供全面的网络结果\n 3. Yahoo Finance API - 用于实时股票和财务数据\n* **Groq API 集成**：使用 qwen-qwq-32b 模型进行超快速 LLM 处理\n* **客户端-服务器架构**：工具管理和 LLM 交互之间的明确分离\n\n## 先决条件\n\n开始之前，请确保您已具备以下条件：\n\n* 从 Python[安装](https://docs.astral.sh/uv/getting-started/installation/)中安装 UV\n* Groq API 密钥。请参阅[文档](https://docs.aicontentlabs.com/articles/groq-api-key/)\n* Tavily API 密钥（在[Tavily AI](https://tavily.com/)注册）\n\n## 安装\n\n1. 克隆存储库：\n\n ```\n git clone https://github.com/dev484p/AgenticAI_MCP\n cd AgenticAI_MCP\n ```\n\n2. 安装依赖项：\n\n ```\n uv add \"mcp[cli]\"\n ```\n\n3. 设置环境变量：在 keys.json 中更新 Groq 和 Tavily api 密钥\n\n4. 可选（使用 MCP Inspector 运行服务器进行开发）：\n\n```\nuv run mcp dev server.py\n```\n\n5. 运行以下命令来启动聊天机器人：\n\n ```\n uv run client.py\n ```\n\n## 可用工具\n\n系统通过MCP服务器提供三种工具：\n\n1. 维基搜索：\n\n* 访问维基百科信息\n* 示例查询：“告诉我人工智能的历史”\n\n2. 互联网搜索（塔维利）：\n\n* 获得全面的网络搜索结果\n* 示例查询：“量子计算的最新发展是什么？”\n\n3. 雅虎财经:\n\n* 获取股票价格和财务数据\n* 示例查询：“AAPL 股票的当前价格是多少？”\n\n## 参考\n\n* \n* \n* 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