[{"_1":2,"_361":-5,"_362":-5},"loaderData",{"_3":4,"_22":23,"_268":269},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":-5},"attributes",[15],"bot","id",17495170,"membership","referenceId","0199ab24-15cd-719b-b50b-df06c33c002b","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_24":25,"_66":-5,"_67":26,"_27":68,"_185":186,"_266":267},"availableLocales",[26,38,45,52,59],{"_27":28,"_31":35,"_36":37},"mcpServer",{"_29":30,"_31":32,"_33":34},"description","A powerful research assistant that conducts intelligent, iterative research through web searches, analysis, and comprehensive report generation on any topic.","name","DeepResearch MCP","readme","# DeepResearch MCP\n\n

\n \n![DeepResearch Logo](https://img.shields.io/badge/DeepResearch-MCP-blue?style=for-the-badge)\n[![TypeScript](https://img.shields.io/badge/TypeScript-007ACC?style=for-the-badge&logo=typescript&logoColor=white)](https://www.typescriptlang.org/)\n[![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge&logo=openai&logoColor=white)](https://openai.com/)\n[![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge&logo=nodedotjs&logoColor=white)](https://nodejs.org/)\n\n

\n\n## 📚 Overview\n\nDeepResearch MCP is a powerful research assistant built on the Model Context Protocol (MCP). It conducts intelligent, iterative research on any topic through web searches, analysis, and comprehensive report generation.\n\n### 🌟 Key Features\n\n- **Intelligent Topic Exploration** - Automatically identifies knowledge gaps and generates focused search queries\n- **Comprehensive Content Extraction** - Enhanced web scraping with improved content organization\n- **Structured Knowledge Processing** - Preserves important information while managing token usage\n- **Scholarly Report Generation** - Creates detailed, well-structured reports with executive summaries, analyses, and visualizations\n- **Complete Bibliography** - Properly cites all sources with numbered references\n- **Adaptive Content Management** - Automatically manages content to stay within token limits\n- **Error Resilience** - Recovers from errors and generates partial reports when full processing isn't possible\n\n## 🛠️ Architecture\n\n

\n \n```\n┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐\n│ │ │ │ │ │\n│ MCP Server Layer ├────►│ Research Service├────►│ Search Service │\n│ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │\n│ │ │ │ │ │\n└────────────────────┘ └─────────┬───────┘ └────────────────┘\n │\n ▼\n ┌─────────────────┐\n │ │\n │ OpenAI Service │\n │ (Analysis/Rpt) │\n │ │\n └─────────────────┘\n```\n\n

\n\n## 💻 Installation\n\n### Prerequisites\n\n- Node.js 18 or higher\n- OpenAI API key\n- Firecrawl API key\n\n### Setup Steps\n\n1. **Clone the repository**\n ```bash\n git clone \n cd deep-research-mcp\n ```\n\n2. **Install dependencies**\n ```bash\n npm install\n ```\n\n3. **Configure environment variables**\n ```bash\n cp .env.example .env\n ```\n \n Edit the `.env` file and add your API keys:\n ```\n OPENAI_API_KEY=sk-your-openai-api-key\n FIRECRAWL_API_KEY=your-firecrawl-api-key\n ```\n\n4. **Build the project**\n ```bash\n npm run build\n ```\n\n## 🚀 Usage\n\n### Running the MCP Server\n\nStart the server on stdio for MCP client connections:\n\n```bash\nnpm start\n```\n\n### Using the Example Client\n\nRun research on a specific topic with a specified depth:\n\n```bash\nnpm run client \"Your research topic\" 3\n```\n\nParameters:\n- First argument: Research topic or query\n- Second argument: Research depth (number of iterations, default: 2)\n- Third argument (optional): \"complete\" to use the complete-research tool (one-step process)\n\nExample:\n```bash\nnpm run client \"the impact of climate change on coral reefs\" 3 complete\n```\n\n### Example Output\n\nThe DeepResearch MCP will produce a comprehensive report that includes:\n\n- **Executive Summary** - Concise overview of the research findings\n- **Introduction** - Context and importance of the research topic\n- **Methodology** - Description of the research approach\n- **Comprehensive Analysis** - Detailed examination of the topic\n- **Comparative Analysis** - Visual comparison of key aspects\n- **Discussion** - Interpretation of findings and implications\n- **Limitations** - Constraints and gaps in the research\n- **Conclusion** - Final insights and recommendations\n- **Bibliography** - Complete list of sources with URLs\n\n## 🔧 MCP Integration\n\n### Available MCP Resources\n\n| Resource Path | Description |\n|--------------|-------------|\n| `research://state/{sessionId}` | Access the current state of a research session |\n| `research://findings/{sessionId}` | Access the collected findings for a session |\n\n### Available MCP Tools\n\n| Tool Name | Description | Parameters |\n|-----------|-------------|------------|\n| `initialize-research` | Start a new research session | `query`: string, `depth`: number |\n| `execute-research-step` | Execute the next research step | `sessionId`: string |\n| `generate-report` | Create a final report | `sessionId`: string, `timeout`: number (optional) |\n| `complete-research` | Execute the entire research process | `query`: string, `depth`: number, `timeout`: number (optional) |\n\n## 🖥️ Claude Desktop Integration\n\nDeepResearch MCP can be integrated with Claude Desktop to provide direct research capabilities to Claude.\n\n### Configuration Steps\n\n1. **Copy the sample configuration**\n ```bash\n cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json\n ```\n\n2. **Edit the configuration file**\n \n Update the path to point to your installation of deep-research-mcp and add your API keys:\n\n ```json\n {\n \"mcpServers\": {\n \"deep-research\": {\n \"command\": \"node\",\n \"args\": [\n \"/absolute/path/to/your/deep-research-mcp/dist/index.js\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"your-firecrawler-api-key\",\n \"OPENAI_API_KEY\": \"your-openai-api-key\"\n }\n }\n }\n }\n ```\n\n3. **Restart Claude Desktop**\n \n After saving the configuration, restart Claude Desktop for the changes to take effect.\n\n4. **Using with Claude Desktop**\n \n Now you can ask Claude to perform research using commands like:\n \n ```\n Can you research the impact of climate change on coral reefs and provide a detailed report?\n ```\n\n## 📋 Sample Client Code\n\n```typescript\nimport { Client } from \"@modelcontextprotocol/sdk/client/index.js\";\nimport { StdioClientTransport } from \"@modelcontextprotocol/sdk/client/stdio.js\";\n\nasync function main() {\n // Connect to the server\n const transport = new StdioClientTransport({\n command: \"node\",\n args: [\"dist/index.js\"]\n });\n\n const client = new Client({ name: \"deep-research-client\", version: \"1.0.0\" });\n await client.connect(transport);\n\n // Initialize research\n const initResult = await client.callTool({\n name: \"initialize-research\",\n arguments: {\n query: \"The impact of artificial intelligence on healthcare\",\n depth: 3\n }\n });\n \n // Parse the response to get sessionId\n const { sessionId } = JSON.parse(initResult.content[0].text);\n \n // Execute steps until complete\n let currentDepth = 0;\n while (currentDepth < 3) {\n const stepResult = await client.callTool({\n name: \"execute-research-step\",\n arguments: { sessionId }\n });\n \n const stepInfo = JSON.parse(stepResult.content[0].text);\n currentDepth = stepInfo.currentDepth;\n \n console.log(`Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}`);\n }\n \n // Generate final report with timeout\n const report = await client.callTool({\n name: \"generate-report\",\n arguments: { \n sessionId,\n timeout: 180000 // 3 minutes timeout\n }\n });\n \n console.log(\"Final Report:\");\n console.log(report.content[0].text);\n}\n\nmain().catch(console.error);\n```\n\n## 🔍 Troubleshooting\n\n### Common Issues\n\n- **Token Limit Exceeded**: For very large research topics, you may encounter OpenAI token limit errors. Try:\n - Reducing the research depth\n - Using more specific queries\n - Breaking complex topics into smaller sub-topics\n\n- **Timeout Errors**: For complex research, the process may time out. Solutions:\n - Increase the timeout parameters in tool calls\n - Use the `complete-research` tool with a longer timeout\n - Process research in smaller chunks\n\n- **API Rate Limits**: If you encounter rate limit errors from OpenAI or Firecrawl:\n - Implement a delay between research steps\n - Use an API key with higher rate limits\n - Retry with exponential backoff\n\n## 📝 License\n\nISC\n\n## 🙏 Acknowledgements\n\n- Built with [Model Context Protocol](https://github.com/mhuggins7278/model-context-protocol)\n- Powered by [OpenAI](https://openai.com/) and [Firecrawl](https://firecrawl.dev/)","English","tag","en-US",{"_27":39,"_31":43,"_36":44},{"_29":40,"_31":41,"_33":42},"Un poderoso asistente de investigación que realiza investigaciones inteligentes e iterativas a través de búsquedas web, análisis y generación de informes completos sobre cualquier tema.","Investigación profunda MCP","# Investigación profunda MCP\n\n![Logotipo de DeepResearch](https://img.shields.io/badge/DeepResearch-MCP-blue?style=for-the-badge) [![Mecanografiado](https://img.shields.io/badge/TypeScript-007ACC?style=for-the-badge\\&logo=typescript\\&logoColor=white)](https://www.typescriptlang.org/) [![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge\\&logo=openai\\&logoColor=white)](https://openai.com/) [![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge\\&logo=nodedotjs\\&logoColor=white)](https://nodejs.org/)\n\n## 📚 Descripción general\n\nDeepResearch MCP es un potente asistente de investigación basado en el Protocolo de Contexto de Modelo (MCP). Realiza investigaciones inteligentes e iterativas sobre cualquier tema mediante búsquedas web, análisis y generación de informes completos.\n\n### 🌟 Características principales\n\n* **Exploración inteligente de temas** : identifica automáticamente las lagunas de conocimiento y genera consultas de búsqueda enfocadas\n* **Extracción integral de contenido** : web scraping mejorado con organización de contenido optimizada\n* **Procesamiento de conocimiento estructurado** : preserva información importante mientras administra el uso de tokens\n* **Generación de informes académicos** : crea informes detallados y bien estructurados con resúmenes ejecutivos, análisis y visualizaciones.\n* **Bibliografía completa** : cita correctamente todas las fuentes con referencias numeradas\n* **Gestión de contenido adaptable** : administra automáticamente el contenido para mantenerse dentro de los límites de tokens.\n* **Resiliencia ante errores** : se recupera de errores y genera informes parciales cuando no es posible el procesamiento completo\n\n## 🛠️ Arquitectura\n\n```\n┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐\n│ │ │ │ │ │\n│ MCP Server Layer ├────►│ Research Service├────►│ Search Service │\n│ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │\n│ │ │ │ │ │\n└────────────────────┘ └─────────┬───────┘ └────────────────┘\n │\n ▼\n ┌─────────────────┐\n │ │\n │ OpenAI Service │\n │ (Analysis/Rpt) │\n │ │\n └─────────────────┘\n```\n\n## 💻 Instalación\n\n### Prerrequisitos\n\n* Node.js 18 o superior\n* Clave API de OpenAI\n* Clave API de Firecrawl\n\n### Pasos de configuración\n\n1. **Clonar el repositorio**\n\n ```bash\n git clone \n cd deep-research-mcp\n ```\n\n2. **Instalar dependencias**\n\n ```bash\n npm install\n ```\n\n3. **Configurar variables de entorno**\n\n ```bash\n cp .env.example .env\n ```\n\n Edite el archivo `.env` y agregue sus claves API:\n\n ```\n OPENAI_API_KEY=sk-your-openai-api-key\n FIRECRAWL_API_KEY=your-firecrawl-api-key\n ```\n\n4. **Construir el proyecto**\n\n ```bash\n npm run build\n ```\n\n## 🚀 Uso\n\n### Ejecución del servidor MCP\n\nInicie el servidor en stdio para conexiones de cliente MCP:\n\n```bash\nnpm start\n```\n\n### Usando el cliente de ejemplo\n\nRealizar una investigación sobre un tema específico con una profundidad determinada:\n\n```bash\nnpm run client \"Your research topic\" 3\n```\n\nParámetros:\n\n* Primer argumento: Tema de investigación o consulta\n* Segundo argumento: Profundidad de la investigación (número de iteraciones, predeterminado: 2)\n* Tercer argumento (opcional): \"completo\" para utilizar la herramienta de investigación completa (proceso de un solo paso)\n\nEjemplo:\n\n```bash\nnpm run client \"the impact of climate change on coral reefs\" 3 complete\n```\n\n### Ejemplo de salida\n\nEl MCP de DeepResearch producirá un informe completo que incluye:\n\n* **Resumen ejecutivo** : descripción general concisa de los resultados de la investigación\n* **Introducción** - Contexto e importancia del tema de investigación\n* **Metodología** - Descripción del enfoque de la investigación\n* **Análisis exhaustivo** : examen detallado del tema\n* **Análisis comparativo** : comparación visual de aspectos clave\n* **Discusión** - Interpretación de los hallazgos e implicaciones\n* **Limitaciones** - Restricciones y lagunas en la investigación\n* **Conclusión** - Conclusiones y recomendaciones finales\n* **Bibliografía** - Lista completa de fuentes con URL\n\n## 🔧 Integración MCP\n\n### Recursos MCP disponibles\n\n| Ruta de recursos | Descripción |\n| --------------------------------- | ------------------------------------------------------- |\n| `research://state/{sessionId}` | Acceder al estado actual de una sesión de investigación |\n| `research://findings/{sessionId}` | Acceda a los resultados recopilados para una sesión |\n\n### Herramientas MCP disponibles\n\n| Nombre de la herramienta | Descripción | Parámetros |\n| ------------------------ | ------------------------------------------- | ----------------------------------------------------------------- |\n| `initialize-research` | Iniciar una nueva sesión de investigación | `query` : cadena, `depth` : número |\n| `execute-research-step` | Ejecutar el siguiente paso de investigación | `sessionId` : cadena |\n| `generate-report` | Crear un informe final | `sessionId` : cadena, `timeout` : número (opcional) |\n| `complete-research` | Ejecutar todo el proceso de investigación | `query` : cadena, `depth` : número, `timeout` : número (opcional) |\n\n## 🖥️ Integración de escritorio de Claude\n\nDeepResearch MCP se puede integrar con Claude Desktop para proporcionar capacidades de investigación directa a Claude.\n\n### Pasos de configuración\n\n1. **Copiar la configuración de muestra**\n\n ```bash\n cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json\n ```\n\n2. **Editar el archivo de configuración**\n\n Actualice la ruta para que apunte a su instalación de deep-research-mcp y agregue sus claves API:\n\n ```json\n {\n \"mcpServers\": {\n \"deep-research\": {\n \"command\": \"node\",\n \"args\": [\n \"/absolute/path/to/your/deep-research-mcp/dist/index.js\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"your-firecrawler-api-key\",\n \"OPENAI_API_KEY\": \"your-openai-api-key\"\n }\n }\n }\n }\n ```\n\n3. **Reiniciar Claude Desktop**\n\n Después de guardar la configuración, reinicie Claude Desktop para que los cambios surtan efecto.\n\n4. **Uso con Claude Desktop**\n\n Ahora puedes pedirle a Claude que realice una investigación usando comandos como:\n\n ```\n Can you research the impact of climate change on coral reefs and provide a detailed report?\n ```\n\n## 📋 Código de cliente de muestra\n\n```typescript\nimport { Client } from \"@modelcontextprotocol/sdk/client/index.js\";\nimport { StdioClientTransport } from \"@modelcontextprotocol/sdk/client/stdio.js\";\n\nasync function main() {\n // Connect to the server\n const transport = new StdioClientTransport({\n command: \"node\",\n args: [\"dist/index.js\"]\n });\n\n const client = new Client({ name: \"deep-research-client\", version: \"1.0.0\" });\n await client.connect(transport);\n\n // Initialize research\n const initResult = await client.callTool({\n name: \"initialize-research\",\n arguments: {\n query: \"The impact of artificial intelligence on healthcare\",\n depth: 3\n }\n });\n \n // Parse the response to get sessionId\n const { sessionId } = JSON.parse(initResult.content[0].text);\n \n // Execute steps until complete\n let currentDepth = 0;\n while (currentDepth < 3) {\n const stepResult = await client.callTool({\n name: \"execute-research-step\",\n arguments: { sessionId }\n });\n \n const stepInfo = JSON.parse(stepResult.content[0].text);\n currentDepth = stepInfo.currentDepth;\n \n console.log(`Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}`);\n }\n \n // Generate final report with timeout\n const report = await client.callTool({\n name: \"generate-report\",\n arguments: { \n sessionId,\n timeout: 180000 // 3 minutes timeout\n }\n });\n \n console.log(\"Final Report:\");\n console.log(report.content[0].text);\n}\n\nmain().catch(console.error);\n```\n\n## 🔍 Solución de problemas\n\n### Problemas comunes\n\n* **Límite de tokens excedido** : En temas de investigación muy extensos, es posible que se produzcan errores de límite de tokens de OpenAI. Pruebe:\n\n * Reducir la profundidad de la investigación\n * Usando consultas más específicas\n * Dividir temas complejos en subtemas más pequeños\n\n* **Errores de tiempo de espera** : En investigaciones complejas, el proceso puede agotar el tiempo de espera. Soluciones:\n\n * Aumentar los parámetros de tiempo de espera en las llamadas de herramientas\n * Utilice la herramienta `complete-research` con un tiempo de espera más largo\n * Investigación de procesos en fragmentos más pequeños\n\n* **Límites de velocidad de la API** : si encuentra errores de límite de velocidad de OpenAI o Firecrawl:\n\n * Implementar un retraso entre los pasos de la investigación\n * Utilice una clave API con límites de velocidad más altos\n * Reintentar con retroceso exponencial\n\n## 📝 Licencia\n\nISC\n\n## 🙏 Agradecimientos\n\n* Construido con [el Protocolo de Contexto de Modelo](https://github.com/mhuggins7278/model-context-protocol)\n* Desarrollado con [OpenAI](https://openai.com/) y [Firecrawl](https://firecrawl.dev/)","Spanish","es-ES",{"_27":46,"_31":50,"_36":51},{"_29":47,"_31":48,"_33":49},"あらゆるトピックについて、Web 検索、分析、包括的なレポート生成を通じてインテリジェントな反復的なリサーチを実行する強力なリサーチアシスタントです。","ディープリサーチMCP","# ディープリサーチMCP\n\n![ディープリサーチロゴ](https://img.shields.io/badge/DeepResearch-MCP-blue?style=for-the-badge) [![タイプスクリプト](https://img.shields.io/badge/TypeScript-007ACC?style=for-the-badge\\&logo=typescript\\&logoColor=white)](https://www.typescriptlang.org/) [![オープンAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge\\&logo=openai\\&logoColor=white)](https://openai.com/) [![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge\\&logo=nodedotjs\\&logoColor=white)](https://nodejs.org/)\n\n## 📚 概要\n\nDeepResearch MCPは、モデルコンテキストプロトコル（MCP）を基盤とする強力なリサーチアシスタントです。Web検索、分析、包括的なレポート生成を通じて、あらゆるトピックに関するインテリジェントで反復的なリサーチを実行します。\n\n### 🌟 主な機能\n\n* **インテリジェントなトピック探索**- 知識のギャップを自動的に特定し、焦点を絞った検索クエリを生成します\n* **包括的なコンテンツ抽出**- コンテンツ構成の改善による強化されたWebスクレイピング\n* **構造化知識処理**- トークンの使用を管理しながら重要な情報を保存します\n* **学術レポート生成**- エグゼクティブサマリー、分析、視覚化を備えた詳細で構造化されたレポートを作成します。\n* **完全な参考文献**- 番号付きの参考文献ですべての情報源を適切に引用する\n* **適応型コンテンツ管理**- トークン制限内に収まるようにコンテンツを自動的に管理します\n* **エラー耐性**- 完全な処理が不可能な場合にエラーから回復し、部分的なレポートを生成します\n\n## 🛠️ 建築\n\n```\n┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐\n│ │ │ │ │ │\n│ MCP Server Layer ├────►│ Research Service├────►│ Search Service │\n│ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │\n│ │ │ │ │ │\n└────────────────────┘ └─────────┬───────┘ └────────────────┘\n │\n ▼\n ┌─────────────────┐\n │ │\n │ OpenAI Service │\n │ (Analysis/Rpt) │\n │ │\n └─────────────────┘\n```\n\n## 💻 インストール\n\n### 前提条件\n\n* Node.js 18以上\n* OpenAI APIキー\n* Firecrawl APIキー\n\n### セットアップ手順\n\n1. **リポジトリをクローンする**\n\n ```bash\n git clone \n cd deep-research-mcp\n ```\n\n2. **依存関係をインストールする**\n\n ```bash\n npm install\n ```\n\n3. **環境変数を設定する**\n\n ```bash\n cp .env.example .env\n ```\n\n `.env`ファイルを編集し、API キーを追加します。\n\n ```\n OPENAI_API_KEY=sk-your-openai-api-key\n FIRECRAWL_API_KEY=your-firecrawl-api-key\n ```\n\n4. **プロジェクトを構築する**\n\n ```bash\n npm run build\n ```\n\n## 🚀 使用方法\n\n### MCPサーバーの実行\n\nMCP クライアント接続用に stdio でサーバーを起動します。\n\n```bash\nnpm start\n```\n\n### サンプルクライアントの使用\n\n特定のトピックについて、指定した深さで調査を実行します。\n\n```bash\nnpm run client \"Your research topic\" 3\n```\n\nパラメータ:\n\n* 最初の議論: 研究テーマまたはクエリ\n* 2番目の引数: 調査の深さ（反復回数、デフォルト: 2）\n* 3 番目の引数（オプション）: 「complete」を指定すると、complete-research ツール（ワンステッププロセス）が使用されます。\n\n例：\n\n```bash\nnpm run client \"the impact of climate change on coral reefs\" 3 complete\n```\n\n### 出力例\n\nDeepResearch MCP は、以下の内容を含む包括的なレポートを作成します。\n\n* **エグゼクティブサマリー**- 研究結果の簡潔な概要\n* **はじめに**- 研究テーマの背景と��要性\n* **方法論**- 研究アプローチの説明\n* **包括的な分析**- トピックの詳細な調査\n* **比較分析**- 主要な側面の視覚的な比較\n* **考察**- 調査結果とその意味の解釈\n* **限界**- 研究における制約とギャップ\n* **結論**- 最終的な洞察と推奨事項\n* **参考文献**- URL を含む完全な情報源リスト\n\n## 🔧 MCP 統合\n\n### 利用可能なMCPリソース\n\n| リソースパス | 説明 |\n| --------------------------------- | -------------------- |\n| `research://state/{sessionId}` | 研究セッションの現在の状態にアクセスする |\n| `research://findings/{sessionId}` | セッションで収集された結果にアクセスする |\n\n### 利用可能なMCPツール\n\n| ツール名 | 説明 | パラメータ |\n| ----------------------- | --------------- | -------------------------------------------------- |\n| `initialize-research` | 新しい研究セッションを開始する | `query` : 文字列、 `depth` : 数値 |\n| `execute-research-step` | 次の研究ステップを実行する | `sessionId` : 文字列 |\n| `generate-report` | 最終レポートを作成する | `sessionId` : 文字列、 `timeout` : 数値（オプション） |\n| `complete-research` | 研究プロセス全体を実行する | `query` : 文字列、 `depth` : 数値、 `timeout` : 数値（オプション） |\n\n## 🖥️ クロードデスクトップ統合\n\nDeepResearch MCP を Claude Desktop と統合して、Claude に直接リサーチ機能を提供できます。\n\n### 設定手順\n\n1. **サンプル構成をコピーする**\n\n ```bash\n cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json\n ```\n\n2. **設定ファイルを編集する**\n\n deep-research-mcp のインストールを指すようにパスを更新し、API キーを追加します。\n\n ```json\n {\n \"mcpServers\": {\n \"deep-research\": {\n \"command\": \"node\",\n \"args\": [\n \"/absolute/path/to/your/deep-research-mcp/dist/index.js\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"your-firecrawler-api-key\",\n \"OPENAI_API_KEY\": \"your-openai-api-key\"\n }\n }\n }\n }\n ```\n\n3. **Claudeデスクトップを再起動します**\n\n 設定を保存した後、変更を有効にするために Claude Desktop を再起動します。\n\n4. **Claude Desktopでの使用**\n\n 次のようなコマンドを使用して、Claude に調査を実行するよう依頼できるようになりました。\n\n ```\n Can you research the impact of climate change on coral reefs and provide a detailed report?\n ```\n\n## 📋 サンプルクライアントコード\n\n```typescript\nimport { Client } from \"@modelcontextprotocol/sdk/client/index.js\";\nimport { StdioClientTransport } from \"@modelcontextprotocol/sdk/client/stdio.js\";\n\nasync function main() {\n // Connect to the server\n const transport = new StdioClientTransport({\n command: \"node\",\n args: [\"dist/index.js\"]\n });\n\n const client = new Client({ name: \"deep-research-client\", version: \"1.0.0\" });\n await client.connect(transport);\n\n // Initialize research\n const initResult = await client.callTool({\n name: \"initialize-research\",\n arguments: {\n query: \"The impact of artificial intelligence on healthcare\",\n depth: 3\n }\n });\n \n // Parse the response to get sessionId\n const { sessionId } = JSON.parse(initResult.content[0].text);\n \n // Execute steps until complete\n let currentDepth = 0;\n while (currentDepth < 3) {\n const stepResult = await client.callTool({\n name: \"execute-research-step\",\n arguments: { sessionId }\n });\n \n const stepInfo = JSON.parse(stepResult.content[0].text);\n currentDepth = stepInfo.currentDepth;\n \n console.log(`Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}`);\n }\n \n // Generate final report with timeout\n const report = await client.callTool({\n name: \"generate-report\",\n arguments: { \n sessionId,\n timeout: 180000 // 3 minutes timeout\n }\n });\n \n console.log(\"Final Report:\");\n console.log(report.content[0].text);\n}\n\nmain().catch(console.error);\n```\n\n## 🔍 トラブルシューティング\n\n### よくある問題\n\n* **トークン制限超過**: 研究トピックが非常に大規模な場合、OpenAI のトークン制限エラーが発生する可能性があります。以下をお試しください。\n\n * 研究の深さを減らす\n * より具体的なクエリを使用する\n * 複雑なトピックをより小さなサブトピックに分割する\n\n* **タイムアウトエラー**: 複雑な調査の場合、プロセスがタイムアウトすることがあります。解決策:\n\n * ツール呼び出しのタイムアウトパラメータを増やす\n * タイムアウトを長くして`complete-research`ツールを使用する\n * プロセス研究を小さな塊に分ける\n\n* **API レート制限**: OpenAI または Firecrawl からレート制限エラーが発生した場合:\n\n * 調査ステップ間の遅延を実装する\n * レート制限の高いAPIキーを使用する\n * 指数バックオフで再試行する\n\n## 📝 ライセンス\n\nISC\n\n## 🙏 謝辞\n\n* [モデルコンテキストプロトコル](https://github.com/mhuggins7278/model-context-protocol)で構築\n* [OpenAI](https://openai.com/)と[Firecrawl](https://firecrawl.dev/)を搭載","Japanese","ja-JP",{"_27":53,"_31":57,"_36":58},{"_29":54,"_31":55,"_33":56},"웹 검색, 분석, 모든 주제에 대한 포괄적인 보고서 생성을 통해 지능적이고 반복적인 연구를 수행하는 강력한 연구 지원자입니다.","딥리서치 MCP","# 딥리서치 MCP\n\n![딥리서치 로고](https://img.shields.io/badge/DeepResearch-MCP-blue?style=for-the-badge) [![타입스크립트](https://img.shields.io/badge/TypeScript-007ACC?style=for-the-badge\\&logo=typescript\\&logoColor=white)](https://www.typescriptlang.org/) [![오픈AI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge\\&logo=openai\\&logoColor=white)](https://openai.com/) [![노드.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge\\&logo=nodedotjs\\&logoColor=white)](https://nodejs.org/)\n\n## 📚 개요\n\nDeepResearch MCP는 모델 컨텍스트 프로토콜(MCP)을 기반으로 구축된 강력한 연구 지원 도구입니다. 웹 검색, 분석 및 종합 보고서 생성을 통해 모든 주제에 대해 지능적이고 반복적인 연구를 수행합니다.\n\n### 🌟 주요 특징\n\n* **지능형 주제 탐색** - 지식 격차를 자동으로 식별하고 집중적인 검색 쿼리를 생성합니다.\n* **포괄적인 콘텐츠 추출** - 향상된 콘텐츠 구성을 통한 향상된 웹 스크래핑\n* **구조화된 지식 처리** - 토큰 사용을 관리하면서 중요한 정보를 보존합니다.\n* **학술 보고서 생성** - 요약, 분석 및 시각화를 통해 상세하고 잘 구성된 보고서를 생성합니다.\n* **완전한 참고문헌** - 번호가 매겨진 참조를 통해 모든 출처를 적절하게 인용합니다.\n* **적응형 콘텐츠 관리** - 토큰 한도 내에 있도록 콘텐츠를 자동으로 관리합니다.\n* **오류 복원력** - 전체 처리가 불가능한 경우 오류를 복구하고 부분 보고서를 생성합니다.\n\n## 🛠️ 건축\n\n지엑스피1\n\n## 💻 설치\n\n### 필수 조건\n\n* Node.js 18 이상\n* OpenAI API 키\n* Firecrawl API 키\n\n### 설정 단계\n\n1. **저장소를 복제합니다**\n\n ```bash\n git clone \n cd deep-research-mcp\n ```\n\n2. **종속성 설치**\n\n ```bash\n npm install\n ```\n\n3. **환경 변수 구성**\n\n ```bash\n cp .env.example .env\n ```\n\n `.env` 파일을 편집하고 API 키를 추가하세요.\n\n ```\n OPENAI_API_KEY=sk-your-openai-api-key\n FIRECRAWL_API_KEY=your-firecrawl-api-key\n ```\n\n4. **프로젝트를 빌드하세요**\n\n ```bash\n npm run build\n ```\n\n## 🚀 사용법\n\n### MCP 서버 실행\n\nMCP 클라이언트 연결을 위해 stdio에서 서버를 시작합니다.\n\n```bash\nnpm start\n```\n\n### 예제 클라이언트 사용\n\n특정 주제에 대해 지정된 깊이로 연구를 실행합니다.\n\n```bash\nnpm run client \"Your research topic\" 3\n```\n\n매개변수:\n\n* 첫 번째 주장: 연구 주제 또는 질의\n* 두 번째 인수: 연구 깊이(반복 횟수, 기본값: 2)\n* 세 번째 인수(선택 사항): 완전한 연구 도구(단일 단계 프로세스)를 사용하려면 \"complete\"를 사용합니다.\n\n예:\n\n```bash\nnpm run client \"the impact of climate change on coral reefs\" 3 complete\n```\n\n### 출력 예\n\nDeepResearch MCP는 다음을 포함하는 포괄적인 보고서를 작성합니다.\n\n* **요약** - 연구 결과에 대한 간략한 개요\n* **서론** - 연구 주제의 맥락 및 중요성\n* **방법론** - 연구 접근 방식에 대한 설명\n* **종합 분석** - 주제에 대한 자세한 검토\n* **비교 분석** - 주요 측면의 시각적 비교\n* **토론** - 연구 결과 및 의미 해석\n* **제한 사항** - 연구의 제약 및 차이점\n* **결론** - 최종 통찰력 및 권장 사항\n* **참고문헌** - URL이 포함된 전체 출처 목록\n\n## 🔧 MCP 통합\n\n### 사용 가능한 MCP 리소스\n\n| 리소스 경로 | 설명 |\n| --------------------------------- | --------------------- |\n| `research://state/{sessionId}` | 연구 세션의 현재 상태에 액세스하세요 |\n| `research://findings/{sessionId}` | 세션에 대한 수집된 결과에 액세스하세요 |\n\n### 사용 가능한 MCP 도구\n\n| 도구 이름 | 설명 | 매개변수 |\n| ----------------------- | ---------------- | -------------------------------------------------- |\n| `initialize-research` | 새로운 연구 세션을 시작하세요 | `query` : 문자열, `depth` : 숫자 |\n| `execute-research-step` | 다음 연구 단계를 실행하세요 | `sessionId` : 문자열 |\n| `generate-report` | 최종 보고서 작성 | `sessionId` : 문자열, `timeout` : 숫자(선택 사항) |\n| `complete-research` | 전체 연구 과정을 실행합니다 | `query` : 문자열, `depth` : 숫자, `timeout` : 숫자(선택 사항) |\n\n## 🖥️ Claude 데스크톱 통합\n\nDeepResearch MCP는 Claude Desktop과 통합되어 Claude에 직접적인 연구 기능을 제공할 수 있습니다.\n\n### 구성 단계\n\n1. **샘플 구성을 복사합니다**\n\n ```bash\n cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json\n ```\n\n2. **구성 파일을 편집하세요**\n\n deep-research-mcp 설치를 가리키도록 경로를 업데이트하고 API 키를 추가합니다.\n\n ```json\n {\n \"mcpServers\": {\n \"deep-research\": {\n \"command\": \"node\",\n \"args\": [\n \"/absolute/path/to/your/deep-research-mcp/dist/index.js\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"your-firecrawler-api-key\",\n \"OPENAI_API_KEY\": \"your-openai-api-key\"\n }\n }\n }\n }\n ```\n\n3. **Claude Desktop을 다시 시작하세요**\n\n 구성을 저장한 후 Claude Desktop을 다시 시작하여 변경 사항을 적용하세요.\n\n4. **Claude Desktop과 함께 사용**\n\n 이�� 다음과 같은 명령을 사용하여 Claude에게 연구를 수행하도록 요청할 수 있습니다.\n\n ```\n Can you research the impact of climate change on coral reefs and provide a detailed report?\n ```\n\n## 📋 샘플 클라이언트 코드\n\n```typescript\nimport { Client } from \"@modelcontextprotocol/sdk/client/index.js\";\nimport { StdioClientTransport } from \"@modelcontextprotocol/sdk/client/stdio.js\";\n\nasync function main() {\n // Connect to the server\n const transport = new StdioClientTransport({\n command: \"node\",\n args: [\"dist/index.js\"]\n });\n\n const client = new Client({ name: \"deep-research-client\", version: \"1.0.0\" });\n await client.connect(transport);\n\n // Initialize research\n const initResult = await client.callTool({\n name: \"initialize-research\",\n arguments: {\n query: \"The impact of artificial intelligence on healthcare\",\n depth: 3\n }\n });\n \n // Parse the response to get sessionId\n const { sessionId } = JSON.parse(initResult.content[0].text);\n \n // Execute steps until complete\n let currentDepth = 0;\n while (currentDepth < 3) {\n const stepResult = await client.callTool({\n name: \"execute-research-step\",\n arguments: { sessionId }\n });\n \n const stepInfo = JSON.parse(stepResult.content[0].text);\n currentDepth = stepInfo.currentDepth;\n \n console.log(`Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}`);\n }\n \n // Generate final report with timeout\n const report = await client.callTool({\n name: \"generate-report\",\n arguments: { \n sessionId,\n timeout: 180000 // 3 minutes timeout\n }\n });\n \n console.log(\"Final Report:\");\n console.log(report.content[0].text);\n}\n\nmain().catch(console.error);\n```\n\n## 🔍 문제 해결\n\n### 일반적인 문제\n\n* **토큰 한도 초과** : 매우 큰 규모의 연구 주제의 경우 OpenAI 토큰 한도 오류가 발생할 수 있습니다. 다음을 시도해 보세요.\n\n * 연구 깊이 줄이기\n * 더 구체적인 쿼리 사용\n * 복잡한 주제를 더 작은 하위 주제로 나누기\n\n* **시간 초과 오류** : 복잡한 조사의 경우, 프로세스가 시간 초과될 수 있습니다. 해결 방법:\n\n * 도구 호출에서 시간 초과 매개변수를 늘리세요\n * 더 긴 시간 제한이 있는 `complete-research` 도구를 사용하세요\n * 더 작은 단위로 연구 과정 진행\n\n* **API 속도 제한** : OpenAI 또는 Firecrawl에서 속도 제한 오류가 발생하는 경우:\n\n * 연구 단계 사이에 지연을 구현합니다.\n * 더 높은 속도 제한이 있는 API 키를 사용하세요\n * 지수 백오프로 재시도\n\n## 📝 라이센스\n\n아이에스씨\n\n## 🙏 감사의 말\n\n* [모델 컨텍스트 프로토콜](https://github.com/mhuggins7278/model-context-protocol) 로 구축됨\n* [OpenAI](https://openai.com/) 및 [Firecrawl](https://firecrawl.dev/) 기반","Korean","ko-KR",{"_27":60,"_31":64,"_36":65},{"_29":61,"_31":62,"_33":63},"强大的研究助手，通过网络搜索、分析和生成任何主题的综合报告进行智能、迭代研究。","深度研究 MCP","# 深度研究 MCP\n\n![DeepResearch 徽标](https://img.shields.io/badge/DeepResearch-MCP-blue?style=for-the-badge) [![TypeScript](https://img.shields.io/badge/TypeScript-007ACC?style=for-the-badge\\&logo=typescript\\&logoColor=white)](https://www.typescriptlang.org/) [![OpenAI](https://img.shields.io/badge/OpenAI-412991?style=for-the-badge\\&logo=openai\\&logoColor=white)](https://openai.com/) [![Node.js](https://img.shields.io/badge/Node.js-339933?style=for-the-badge\\&logo=nodedotjs\\&logoColor=white)](https://nodejs.org/)\n\n## 📚 概述\n\nDeepResearch MCP 是一款基于模型上下文协议 (MCP) 构建的强大研究助手。它通过网络搜索、分析和综合报告生成，对任何主题进行智能迭代研究。\n\n### 🌟 主要特点\n\n* **智能主题探索**——自动识别知识差距并生成重点搜索查询\n* **全面的内容提取**——增强网页抓取功能，改进内容组织\n* **结构化知识处理**——在管理令牌使用的同时保留重要信息\n* **学术报告生成**——创建包含执行摘要、分析和可视化内容的详细、结构良好的报告\n* **完整的参考书目**- 正确引用所有来源并附有编号\n* **自适应内容管理**- 自动管理内容以保持在令牌限制内\n* **错误恢复**——当无法进行完整处理时，从错误中恢复并生成部分报告\n\n## 🛠️ 建筑\n\n```\n┌────────────────────┐ ┌─────────────────┐ ┌────────────────┐\n│ │ │ │ │ │\n│ MCP Server Layer ├────►│ Research Service├────►│ Search Service │\n│ (Tools & Prompts) │ │ (Session Mgmt) │ │ (Firecrawl) │\n│ │ │ │ │ │\n└────────────────────┘ └─────────┬───────┘ └────────────────┘\n │\n ▼\n ┌─────────────────┐\n │ │\n │ OpenAI Service │\n │ (Analysis/Rpt) │\n │ │\n └─────────────────┘\n```\n\n## 💻安装\n\n### 先决条件\n\n* Node.js 18 或更高版本\n* OpenAI API 密钥\n* Firecrawl API 密钥\n\n### 设置步骤\n\n1. **克隆存储库**\n\n ```bash\n git clone \n cd deep-research-mcp\n ```\n\n2. **安装依赖项**\n\n ```bash\n npm install\n ```\n\n3. **配置环境变量**\n\n ```bash\n cp .env.example .env\n ```\n\n 编辑`.env`文件并添加您的 API 密钥：\n\n ```\n OPENAI_API_KEY=sk-your-openai-api-key\n FIRECRAWL_API_KEY=your-firecrawl-api-key\n ```\n\n4. **构建项目**\n\n ```bash\n npm run build\n ```\n\n## 🚀 使用方法\n\n### 运行 MCP 服务器\n\n在 stdio 上启动服务器以进行 MCP 客户端连接：\n\n```bash\nnpm start\n```\n\n### 使用示例客户端\n\n对特定主题进行指定深度的研究：\n\n```bash\nnpm run client \"Your research topic\" 3\n```\n\n参数：\n\n* 第一个参数：研究主题或查询\n* 第二个参数：研究深度（迭代次数，默认值：2）\n* 第三个参数（可选）：“complete” 使用完整研究工具（一步过程）\n\n例子：\n\n```bash\nnpm run client \"the impact of climate change on coral reefs\" 3 complete\n```\n\n### 示例输出\n\nDeepResearch MCP 将生成一份综合报告，其中包括：\n\n* **执行摘要**——研究结果的简要概述\n* **引言**——研究主题的背景和重要性\n* **方法论**——研究方法的描述\n* **综合分析**——对主题进行详细检查\n* **比较分析**——关键方面的视觉比较\n* **讨论**——研究结果和含义的解释\n* **局限性**——研究中的制约因素和差距\n* **结论**- 最终见解和建议\n* **参考书目**- 包含 URL 的完整来源列表\n\n## 🔧 MCP 集成\n\n### 可用的 MCP 资源\n\n| 资源路径 | 描述 |\n| --------------------------------- | ----------- |\n| `research://state/{sessionId}` | 访问研究会话的当前状态 |\n| `research://findings/{sessionId}` | 访问会话收集到的结果 |\n\n### 可用的 MCP 工具\n\n| 工具名称 | 描述 | 参数 |\n| ----------------------- | -------- | -------------------------------------------- |\n| `initialize-research` | 开始新的研究会话 | `query` ：字符串， `depth` ：数字 |\n| `execute-research-step` | 执行下一步研究 | `sessionId` ：字符串 |\n| `generate-report` | 创建最终报告 | `sessionId` ：字符串， `timeout` ：数字（可选） |\n| `complete-research` | 执行整个研究过程 | `query` ：字符串， `depth` ：数字， `timeout` ：数字（可选） |\n\n## 🖥️ Claude 桌面集成\n\nDeepResearch MCP 可以与 Claude Desktop 集成，为 Claude 提供直接研究能力。\n\n### 配置步骤\n\n1. **复制示例配置**\n\n ```bash\n cp claude_desktop_config_sample.json ~/path/to/claude/desktop/config/directory/claude_desktop_config.json\n ```\n\n2. **编辑配置文件**\n\n 更新路径以指向 deep-research-mcp 的安装并添加您的 API 密钥：\n\n ```json\n {\n \"mcpServers\": {\n \"deep-research\": {\n \"command\": \"node\",\n \"args\": [\n \"/absolute/path/to/your/deep-research-mcp/dist/index.js\"\n ],\n \"env\": {\n \"FIRECRAWL_API_KEY\": \"your-firecrawler-api-key\",\n \"OPENAI_API_KEY\": \"your-openai-api-key\"\n }\n }\n }\n }\n ```\n\n3. **重启Claude桌面**\n\n 保存配置后，重新启动 Claude Desktop 以使更改生效。\n\n4. **与 Claude Desktop 一起使用**\n\n 现在你可以要求 Claude 使用以下命令进行研究：\n\n ```\n Can you research the impact of climate change on coral reefs and provide a detailed report?\n ```\n\n## 📋 示例客户端代码\n\n```typescript\nimport { Client } from \"@modelcontextprotocol/sdk/client/index.js\";\nimport { StdioClientTransport } from \"@modelcontextprotocol/sdk/client/stdio.js\";\n\nasync function main() {\n // Connect to the server\n const transport = new StdioClientTransport({\n command: \"node\",\n args: [\"dist/index.js\"]\n });\n\n const client = new Client({ name: \"deep-research-client\", version: \"1.0.0\" });\n await client.connect(transport);\n\n // Initialize research\n const initResult = await client.callTool({\n name: \"initialize-research\",\n arguments: {\n query: \"The impact of artificial intelligence on healthcare\",\n depth: 3\n }\n });\n \n // Parse the response to get sessionId\n const { sessionId } = JSON.parse(initResult.content[0].text);\n \n // Execute steps until complete\n let currentDepth = 0;\n while (currentDepth < 3) {\n const stepResult = await client.callTool({\n name: \"execute-research-step\",\n arguments: { sessionId }\n });\n \n const stepInfo = JSON.parse(stepResult.content[0].text);\n currentDepth = stepInfo.currentDepth;\n \n console.log(`Completed step ${stepInfo.currentDepth}/${stepInfo.maxDepth}`);\n }\n \n // Generate final report with timeout\n const report = await client.callTool({\n name: \"generate-report\",\n arguments: { \n sessionId,\n timeout: 180000 // 3 minutes timeout\n }\n });\n \n console.log(\"Final Report:\");\n console.log(report.content[0].text);\n}\n\nmain().catch(console.error);\n```\n\n## 🔍 故障排除\n\n### 常见问题\n\n* **超出令牌限制**：对于非常大的研究主题，您可能会遇到 OpenAI 令牌限制错误。请尝试：\n\n * 降低研究深度\n * 使用更具体的查询\n * 将复杂的主题分解成更小的子主题\n\n* **超时错误**：对于复杂的研究，该过程可能会超时。解决方案：\n\n * 增加工具调用中的超时参数\n * 使用超时时间较长的`complete-research`工具\n * 以更小的块进行研究\n\n* **API 速率限制**：如果您遇到来自 OpenAI 或 Firecrawl 的速率限制错误：\n\n * 在研究步骤之间实施延迟\n * 使用具有更高速率限制的 API 密钥\n * 使用指数退避算法重试\n\n## 📝 许可证\n\n国际学习中心\n\n## 🙏 致谢\n\n* 使用[模型上下文协议](https://github.com/mhuggins7278/model-context-protocol)构建\n* 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