[{"_1":2,"_363":-5,"_364":-5},"loaderData",{"_3":4,"_29":30,"_270":271},"root",{"_5":6,"_14":15},"toasts",[7],{"_8":9,"_10":11,"_12":13},"uid","019a2315-ef07-7068-a5c2-54a5e489fd75","title","You need to sign in to access this page","type","error","visitor",{"_16":17,"_18":19},"viewportWidth",1280,"visitorSession",{"_20":21,"_23":24,"_25":-5,"_26":27,"_28":-5},"attributes",[22],"bot","id",21005410,"membership","referenceId","019a17bf-377c-71fd-947e-fd17d1ca8e82","userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_31":32,"_73":-5,"_74":33,"_34":75,"_188":189,"_268":269},"availableLocales",[33,45,52,59,66],{"_34":35,"_38":42,"_43":44},"mcpServer",{"_36":37,"_38":39,"_40":41},"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",{"_34":46,"_38":50,"_43":51},{"_36":47,"_38":48,"_40":49},"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",{"_34":53,"_38":57,"_43":58},{"_36":54,"_38":55,"_40":56},"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重要性\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",{"_34":60,"_38":64,"_43":65},{"_36":61,"_38":62,"_40":63},"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 이제 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",{"_34":67,"_38":71,"_43":72},{"_36":68,"_38":69,"_40":70},"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* 由[OpenAI](https://openai.com/)和[Firecrawl](https://firecrawl.dev/)提供支持","Chinese","zh-CN","githubUser","locale",{"_76":77,"_20":78,"_87":37,"_88":37,"_23":89,"_90":91,"_38":39,"_123":124,"_131":-5,"_132":-5,"_133":134,"_176":177,"_182":-5,"_98":163,"_183":184,"_8":185,"_186":187},"addedAt","2025-03-25T19:11:10.580694Z",[79,80,81,82,83,84,85,86],"category:search","category:research-and-data","category:web-scraping","environment:network","hosting:remote-capable","language:typescript","security_grade:a","quality_grade:a","descriptionMarkdown","descriptionPlainText",3673,"integrations",[92,102,109,116],{"_93":94,"_36":100,"_8":101},"brand",{"_95":96,"_38":97,"_98":99},"logoSvgPath","M24 0v24H0V0h24ZM10.933 15.89H6.84v5.52h4.198v-.93H7.955v-1.503h2.77v-.93h-2.77v-1.224h2.978v-.934Zm2.146 0h-1.084v5.52h1.035v-3.6l2.226 3.6h1.118v-5.52h-1.036v3.686l-2.259-3.687Zm5.117 0h-1.208l1.973 5.52h1.19l1.976-5.52h-1.182l-1.352 4.085-1.397-4.086ZM5.4 19.68H3.72v1.68H5.4v-1.68Z",".ENV","slug","dotenv","Uses environment variables for configuration of API keys and other settings, with support for .env files","jtl5h4ji41",{"_93":103,"_36":107,"_8":108},{"_95":104,"_38":105,"_98":106},"M11.998,24c-0.321,0-0.641-0.084-0.922-0.247l-2.936-1.737c-0.438-0.245-0.224-0.332-0.08-0.383 c0.585-0.203,0.703-0.25,1.328-0.604c0.065-0.037,0.151-0.023,0.218,0.017l2.256,1.339c0.082,0.045,0.197,0.045,0.272,0l8.795-5.076 c0.082-0.047,0.134-0.141,0.134-0.238V6.921c0-0.099-0.053-0.192-0.137-0.242l-8.791-5.072c-0.081-0.047-0.189-0.047-0.271,0 L3.075,6.68C2.99,6.729,2.936,6.825,2.936,6.921v10.15c0,0.097,0.054,0.189,0.139,0.235l2.409,1.392 c1.307,0.654,2.108-0.116,2.108-0.89V7.787c0-0.142,0.114-0.253,0.256-0.253h1.115c0.139,0,0.255,0.112,0.255,0.253v10.021 c0,1.745-0.95,2.745-2.604,2.745c-0.508,0-0.909,0-2.026-0.551L2.28,18.675c-0.57-0.329-0.922-0.945-0.922-1.604V6.921 c0-0.659,0.353-1.275,0.922-1.603l8.795-5.082c0.557-0.315,1.296-0.315,1.848,0l8.794,5.082c0.57,0.329,0.924,0.944,0.924,1.603 v10.15c0,0.659-0.354,1.273-0.924,1.604l-8.794,5.078C12.643,23.916,12.324,24,11.998,24z M19.099,13.993 c0-1.9-1.284-2.406-3.987-2.763c-2.731-0.361-3.009-0.548-3.009-1.187c0-0.528,0.235-1.233,2.258-1.233 c1.807,0,2.473,0.389,2.747,1.607c0.024,0.115,0.129,0.199,0.247,0.199h1.141c0.071,0,0.138-0.031,0.186-0.081 c0.048-0.054,0.074-0.123,0.067-0.196c-0.177-2.098-1.571-3.076-4.388-3.076c-2.508,0-4.004,1.058-4.004,2.833 c0,1.925,1.488,2.457,3.895,2.695c2.88,0.282,3.103,0.703,3.103,1.269c0,0.983-0.789,1.402-2.642,1.402 c-2.327,0-2.839-0.584-3.011-1.742c-0.02-0.124-0.126-0.215-0.253-0.215h-1.137c-0.141,0-0.254,0.112-0.254,0.253 c0,1.482,0.806,3.248,4.655,3.248C17.501,17.007,19.099,15.91,19.099,13.993z","Node.js","node-dot-js","Runs on Node.js as the underlying runtime environment, requiring Node.js 18 or higher for operation","l61blzra9e",{"_93":110,"_36":114,"_8":115},{"_95":111,"_38":112,"_98":113},"M22.2819 9.8211a5.9847 5.9847 0 0 0-.5157-4.9108 6.0462 6.0462 0 0 0-6.5098-2.9A6.0651 6.0651 0 0 0 4.9807 4.1818a5.9847 5.9847 0 0 0-3.9977 2.9 6.0462 6.0462 0 0 0 .7427 7.0966 5.98 5.98 0 0 0 .511 4.9107 6.051 6.051 0 0 0 6.5146 2.9001A5.9847 5.9847 0 0 0 13.2599 24a6.0557 6.0557 0 0 0 5.7718-4.2058 5.9894 5.9894 0 0 0 3.9977-2.9001 6.0557 6.0557 0 0 0-.7475-7.0729zm-9.022 12.6081a4.4755 4.4755 0 0 1-2.8764-1.0408l.1419-.0804 4.7783-2.7582a.7948.7948 0 0 0 .3927-.6813v-6.7369l2.02 1.1686a.071.071 0 0 1 .038.052v5.5826a4.504 4.504 0 0 1-4.4945 4.4944zm-9.6607-4.1254a4.4708 4.4708 0 0 1-.5346-3.0137l.142.0852 4.783 2.7582a.7712.7712 0 0 0 .7806 0l5.8428-3.3685v2.3324a.0804.0804 0 0 1-.0332.0615L9.74 19.9502a4.4992 4.4992 0 0 1-6.1408-1.6464zM2.3408 7.8956a4.485 4.485 0 0 1 2.3655-1.9728V11.6a.7664.7664 0 0 0 .3879.6765l5.8144 3.3543-2.0201 1.1685a.0757.0757 0 0 1-.071 0l-4.8303-2.7865A4.504 4.504 0 0 1 2.3408 7.872zm16.5963 3.8558L13.1038 8.364 15.1192 7.2a.0757.0757 0 0 1 .071 0l4.8303 2.7913a4.4944 4.4944 0 0 1-.6765 8.1042v-5.6772a.79.79 0 0 0-.407-.667zm2.0107-3.0231l-.142-.0852-4.7735-2.7818a.7759.7759 0 0 0-.7854 0L9.409 9.2297V6.8974a.0662.0662 0 0 1 .0284-.0615l4.8303-2.7866a4.4992 4.4992 0 0 1 6.6802 4.66zM8.3065 12.863l-2.02-1.1638a.0804.0804 0 0 1-.038-.0567V6.0742a4.4992 4.4992 0 0 1 7.3757-3.4537l-.142.0805L8.704 5.459a.7948.7948 0 0 0-.3927.6813zm1.0976-2.3654l2.602-1.4998 2.6069 1.4998v2.9994l-2.5974 1.4997-2.6067-1.4997Z","OpenAI","openai","Leverages OpenAI for analysis and report generation as part of the research workflow, processing collected information into structured knowledge","2ebyi35s8f",{"_93":117,"_36":121,"_8":122},{"_95":118,"_38":119,"_98":120},"M1.125 0C.502 0 0 .502 0 1.125v21.75C0 23.498.502 24 1.125 24h21.75c.623 0 1.125-.502 1.125-1.125V1.125C24 .502 23.498 0 22.875 0zm17.363 9.75c.612 0 1.154.037 1.627.111a6.38 6.38 0 0 1 1.306.34v2.458a3.95 3.95 0 0 0-.643-.361 5.093 5.093 0 0 0-.717-.26 5.453 5.453 0 0 0-1.426-.2c-.3 0-.573.028-.819.086a2.1 2.1 0 0 0-.623.242c-.17.104-.3.229-.393.374a.888.888 0 0 0-.14.49c0 .196.053.373.156.529.104.156.252.304.443.444s.423.276.696.41c.273.135.582.274.926.416.47.197.892.407 1.266.628.374.222.695.473.963.753.268.279.472.598.614.957.142.359.214.776.214 1.253 0 .657-.125 1.21-.373 1.656a3.033 3.033 0 0 1-1.012 1.085 4.38 4.38 0 0 1-1.487.596c-.566.12-1.163.18-1.79.18a9.916 9.916 0 0 1-1.84-.164 5.544 5.544 0 0 1-1.512-.493v-2.63a5.033 5.033 0 0 0 3.237 1.2c.333 0 .624-.03.872-.09.249-.06.456-.144.623-.25.166-.108.29-.234.373-.38a1.023 1.023 0 0 0-.074-1.089 2.12 2.12 0 0 0-.537-.5 5.597 5.597 0 0 0-.807-.444 27.72 27.72 0 0 0-1.007-.436c-.918-.383-1.602-.852-2.053-1.405-.45-.553-.676-1.222-.676-2.005 0-.614.123-1.141.369-1.582.246-.441.58-.804 1.004-1.089a4.494 4.494 0 0 1 1.47-.629 7.536 7.536 0 0 1 1.77-.201zm-15.113.188h9.563v2.166H9.506v9.646H6.789v-9.646H3.375z","TypeScript","typescript","Built using TypeScript for type-safe implementation of the research assistant functionality","kev5b0kbsc","namespace",{"_125":126,"_98":130},"owner",{"_127":128},"githubAccount",{"_129":130},"login","ameeralns","readmeMarkdown","redditPostExternalId","repository",{"_135":136,"_149":150,"_153":154,"_171":-5,"_172":173,"_174":-5,"_175":-5},"argumentsJsonSchema",{"_137":138,"_146":147,"_12":148},"properties",{"_139":140,"_143":144},"OPENAI_API_KEY",{"_36":141,"_12":142},"Your OpenAI API key","string","FIRECRAWL_API_KEY",{"_36":145,"_12":142},"Your Firecrawl API key","required",[139,143],"object","githubProject",{"_40":41,"_151":152},"url","https://github.com/ameeralns/deepresearchmcp","githubRepository",{"_155":156,"_157":158,"_159":160,"_23":161,"_162":119,"_38":163,"_125":164,"_166":167,"_168":-5,"_169":170},"defaultBranch","main","fullName","ameeralns/DeepResearchMCP","githubTreeId",327107,6150,"language","DeepResearchMCP",{"_129":130,"_8":165},"LpLGokLu46","recentStargazers",1,"spdxLicense","stargazers",23,"glamaJson","inspectable",true,"npmPackage","supportedPlatforms","scores",{"_178":-5,"_179":180,"_181":180},"license","quality",100,"security","simpleIcon","toolCount",4,"fstxi0xu1t","updatedAt","2025-10-01T12:10:39.414110Z","meta",[190,192,194,201,204,206,209,212,215,218,221,223,226,229,231,233,235,237,239],{"_10":191},"DeepResearch MCP | Glama",{"_193":37,"_38":36},"content",{"_195":196,"_197":198,"_199":200},"href","https://glama.ai/mcp/servers/@ameeralns/DeepResearchMCP","rel","canonical","tagName","link",{"_193":39,"_202":203},"property","og:title",{"_193":37,"_202":205},"og:description",{"_193":207,"_202":208},"https://glama.ai/logo.png","og:logo",{"_193":210,"_202":211},"https://glama.ai/mcp/servers/@ameeralns/DeepResearchMCP/og-image","og:image",{"_193":213,"_202":214},"630","og:image:height",{"_193":216,"_202":217},"1200","og:image:width",{"_193":219,"_202":220},"website","og:type",{"_193":196,"_202":222},"og:url",{"_193":224,"_202":225},"Glama – MCP Hosting Platform","og:site_name",{"_193":227,"_38":228},"summary_large_image","twitter:card",{"_193":224,"_38":230},"twitter:title",{"_193":37,"_38":232},"twitter:description",{"_193":210,"_38":234},"twitter:image",{"_193":213,"_38":236},"twitter:image:height",{"_193":216,"_38":238},"twitter:image:width",{"_240":241},"script:ld+json",{"_242":243,"_244":245},"@context","http://schema.org","@graph",[246],{"_242":247,"_248":249,"_250":251},"https://schema.org","@type","BreadcrumbList","itemListElement",[252,258,262,266],{"_248":253,"_254":255,"_38":256,"_257":167},"ListItem","item","/","Glama","position",{"_248":253,"_254":259,"_38":260,"_257":261},"/mcp","MCP",2,{"_248":253,"_254":263,"_38":264,"_257":265},"/mcp/servers","Servers",3,{"_248":253,"_254":267,"_38":39,"_257":184},"/mcp/servers/@ameeralns/DeepResearchMCP","visitorIsMaintainer",false,"routes/_public/mcp/servers/~namespace/~slug/_pages/_explorer/blob/~commit/~/_route",{"_272":273,"_34":279,"_303":304,"_188":315,"_313":339,"_340":341},"blob",{"_193":274,"_275":276,"_277":278},"# OpenAI API Key\nOPENAI_API_KEY=sk-your-openai-api-key\n\n# Firecrawl API Key\nFIRECRAWL_API_KEY=your-firecrawl-api-key\n\n# Server Configuration\nPORT=3000\nMCP_SERVER_NAME=DeepResearch\nMCP_SERVER_VERSION=1.0.0 ","fileName",".env.example","size",205,{"_76":77,"_20":280,"_87":37,"_88":37,"_23":89,"_90":281,"_38":39,"_123":290,"_131":-5,"_132":-5,"_133":293,"_176":302,"_182":-5,"_98":163,"_183":184,"_8":185,"_186":187},[79,80,81,82,83,84,85,86],[282,284,286,288],{"_93":283,"_36":100,"_8":101},{"_95":96,"_38":97,"_98":99},{"_93":285,"_36":107,"_8":108},{"_95":104,"_38":105,"_98":106},{"_93":287,"_36":114,"_8":115},{"_95":111,"_38":112,"_98":113},{"_93":289,"_36":121,"_8":122},{"_95":118,"_38":119,"_98":120},{"_125":291,"_98":130},{"_127":292},{"_129":130},{"_135":294,"_149":299,"_153":300,"_171":-5,"_172":173,"_174":-5,"_175":-5},{"_137":295,"_146":298,"_12":148},{"_139":296,"_143":297},{"_36":141,"_12":142},{"_36":145,"_12":142},[139,143],{"_40":41,"_151":152},{"_155":156,"_157":158,"_159":160,"_23":161,"_162":119,"_38":163,"_125":301,"_166":167,"_168":-5,"_169":170},{"_129":130,"_8":165},{"_178":-5,"_179":180,"_181":180},"member",{"_305":306,"_309":310,"_311":276,"_159":160,"_312":269,"_313":276,"_314":173,"_277":278,"_12":272},"commit",{"_307":308},"sha","c90c37ad13abcfa35034c83f1c63440ecd802ca2","contentStoredObjectPointerId",10199622,"fullPath","indexable","path","safeToDisplayAsText",[316,318,320,322,324,325,326,328,329,330,331,332,333,334,335,336,337,338],{"_10":317},".env.example | DeepResearch MCP | Glama",{"_193":319,"_38":36},"Enterprise-grade security, privacy, and features like agents, MCP, prompt templates, and more.",{"_195":321,"_197":198,"_199":200},"https://glama.ai/mcp/servers/@ameeralns/DeepResearchMCP/blob/c90c37ad13abcfa35034c83f1c63440ecd802ca2/",{"_193":323,"_202":203},".env.example | DeepResearch MCP",{"_193":319,"_202":205},{"_193":207,"_202":208},{"_193":327,"_202":211},"https://glama.ai/generated-images/og?title=.env.example+%7C+DeepResearch+MCP",{"_193":213,"_202":214},{"_193":216,"_202":217},{"_193":219,"_202":220},{"_193":321,"_202":222},{"_193":224,"_202":225},{"_193":227,"_38":228},{"_193":224,"_38":230},{"_193":319,"_38":232},{"_193":327,"_38":234},{"_193":213,"_38":236},{"_193":216,"_38":238},"","treeMembers",[342,346,349,351,354,357,360],{"_305":343,"_311":344,"_313":344,"_12":345},{"_307":308},"src","tree",{"_305":347,"_311":348,"_313":348,"_12":272},{"_307":308},"claude_desktop_config_sample.json",{"_305":350,"_311":276,"_313":276,"_12":272},{"_307":308},{"_305":352,"_311":353,"_313":353,"_12":272},{"_307":308},".gitignore",{"_305":355,"_311":356,"_313":356,"_12":272},{"_307":308},"package.json",{"_305":358,"_311":359,"_313":359,"_12":272},{"_307":308},"README.md",{"_305":361,"_311":362,"_313":362,"_12":272},{"_307":308},"tsconfig.json","actionData","errors"]

MCP directory API