[{"_1":2,"_330":-5,"_331":-5},"loaderData",{"_3":4,"_22":23,"_24":25,"_272":273},"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",7058884,"membership","referenceId","01971406-43e3-74e8-b880-2f000258007a","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_82":-5,"_83":40,"_29":84,"_189":190,"_271":143},"availableLocales",[28,40,47,54,61,68,75],{"_29":30,"_33":37,"_38":39},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","Erweitert LLM-Anwendungen um umfassende autonome Web-Recherchefunktionen und liefert durch die Untersuchung und Validierung zahlreicher vertrauenswürdiger Quellen qualitativ hochwertigere Informationen als herkömmliche Suchtools.","name","GPT Researcher MCP-Server","readme","# 🔍 GPT Researcher MCP-Server\n\n[![Webseite](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![Dokumentation](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![Discord Folgen](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## Warum GPT Researcher MCP?\n\nWährend LLM-Apps mit MCP auf Websuchtools zugreifen können, **liefert GPT Researcher MCP umfassende Rechercheergebnisse.** Standardsuchtools liefern Rohergebnisse, die manuell gefiltert werden müssen, oft irrelevante Quellen enthalten und Platz im Kontextfenster verschwenden.\n\nGPT Researcher durchsucht und validiert selbstständig zahlreiche Quellen und konzentriert sich dabei ausschließlich auf relevante, vertrauenswürdige und aktuelle Informationen. Obwohl es etwas langsamer als die Standardsuche ist (ca. 30 Sekunden Wartezeit), bietet es:\n\n* ✨ Hochwertigere Informationen\n* 📊 Optimierte Kontextnutzung\n* 🔎 Umfassende Ergebnisse\n* 🧠 Bessere Argumentation für LLMs\n\n## 💻 Claude Desktop-Demo\n\n\n\n### Ressourcen\n\n* `research_resource` : Erhalten Sie durch Recherche Webressourcen, die mit einer bestimmten Aufgabe in Zusammenhang stehen.\n\n### Primäre Werkzeuge\n\n* `deep_research` : Führt eine Deep-Web-Recherche zu einem Thema durch und findet die zuverlässigsten und relevantesten Informationen\n* `quick_search` : Führt eine schnelle Websuche durch, die auf Geschwindigkeit statt Qualität optimiert ist und Suchergebnisse mit Snippets liefert. Unterstützt alle GPTR-fähigen Web-Retriever wie Tavily, Bing, Google usw. Erfahren Sie [hier](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers) mehr.\n* `write_report` : Erstellen Sie einen Bericht basierend auf den Forschungsergebnissen\n* `get_research_sources` : Holen Sie sich die in der Forschung verwendeten Quellen\n* `get_research_context` : Holen Sie sich den vollständigen Kontext der Forschung\n\n### Eingabeaufforderungen\n\n* `research_query` : Erstellen Sie eine Eingabeaufforderung für eine Forschungsabfrage\n\n## Voraussetzungen\n\nStellen Sie vor dem Ausführen des MCP-Servers sicher, dass Sie über Folgendes verfügen:\n\n1. Python 3.11 oder höher installiert\n * **Wichtig** : GPT Researcher >=0.12.16 erfordert Python 3.11+\n2. API-Schlüssel für die Dienste, die Sie verwenden möchten:\n * [OpenAI-API-Schlüssel](https://platform.openai.com/api-keys)\n * [Tavily API-Schlüssel](https://app.tavily.com)\n\n## ⚙️ Installation\n\n1. Klonen Sie das GPT Researcher-Repository:\n\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. Installieren Sie die gptr-mcp-Abhängigkeiten:\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. Richten Sie Ihre Umgebungsvariablen ein:\n * Kopieren Sie die Datei `.env.example` , um eine neue Datei mit dem Namen `.env` : GXP3 zu erstellen.\n * Bearbeiten Sie die `.env` Datei, fügen Sie Ihre API-Schlüssel hinzu und konfigurieren Sie andere Einstellungen: GXP4\n\nSie können Ihrer GPT Researcher-Konfiguration auch beliebige andere Umgebungsvariablen hinzufügen.\n\n## 🚀 Ausführen des MCP-Servers\n\nSie können den MCP-Server auf verschiedene Arten ausführen:\n\n### Methode 1: Direkt mit Python\n\n```bash\npython server.py\n```\n\n### Methode 2: Verwenden der MCP-CLI (falls installiert)\n\n```bash\nmcp run server.py\n```\n\n### Methode 3: Verwenden von Docker (für die Produktion empfohlen)\n\n#### Option A: Standalone-Modus\n\nDies ist die einfachste Möglichkeit, den Server auszuführen, wenn Sie keine Verbindung zu anderen Containern herstellen müssen:\n\n```bash\n# Build and run with docker-compose\ndocker-compose -f docker-compose.standalone.yml up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n#### Option B: Mit Netzwerk für n8n-Integration\n\nWenn Sie eine Verbindung zu anderen Diensten wie n8n im selben Netzwerk herstellen müssen:\n\n```bash\n# Create the network if it doesn't exist\ndocker network create n8n-mcp-net\n\n# Build and run with docker-compose\ndocker-compose up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n --network n8n-mcp-net \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n**Hinweis** : Das Docker-Image verwendet Python 3.11, um die Anforderungen von gpt-researcher >=0.12.16 zu erfüllen. Sollten während des Builds Fehler auftreten, stellen Sie sicher, dass Sie die neueste Docker-Datei aus diesem Repository verwenden.\n\nSobald der Server läuft, wird eine Ausgabe angezeigt, die anzeigt, dass er bereit ist, Verbindungen anzunehmen. Sie können die Funktion folgendermaßen überprüfen:\n\n1. Zugriff auf die OpenAPI-Dokumente unter \n2. Testen des MCP-Endpunkts unter \n\n## Integration mit Claude\n\nSie können Ihren MCP-Server mit Claude integrieren, indem Sie Folgendes verwenden:\n\n**[Claude Desktop Integration](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)** – Zur Verwendung mit der Claude-Desktopanwendung auf dem Mac\n\nFür detaillierte Anweisungen folgen Sie dem Link oben.\n\n## 💻 Claude Desktop-Integration\n\nUm Ihren lokal laufenden MCP-Server mit Claude für Mac zu integrieren, müssen Sie:\n\n1. Stellen Sie sicher, dass der MCP-Server installiert und ausgeführt wird\n2. Claude Desktop konfigurieren:\n * Suchen oder erstellen Sie die Konfigurationsdatei unter `~/Library/Application Support/Claude/claude_desktop_config.json`\n * Fügen Sie Ihren lokalen GPT Researcher MCP-Server zur Konfiguration hinzu\n * Starten Sie Claude neu, um die Konfiguration anzuwenden\n\nVollständige Schritt-für-Schritt-Anleitungen finden Sie im [Claude Desktop Integration Guide](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration) .\n\n## 📝 Beispielverwendung mit Claude\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 Fehlerbehebung\n\nWenn beim Ausführen des MCP-Servers Probleme auftreten:\n\n1. Stellen Sie sicher, dass Ihre API-Schlüssel in der `.env` Datei korrekt eingestellt sind\n2. Überprüfen Sie, ob Sie Python 3.11 oder höher verwenden (erforderlich für gpt-researcher >=0.12.16)\n3. Stellen Sie sicher, dass alle Abhängigkeiten korrekt installiert sind\n4. Überprüfen Sie die Serverprotokolle auf Fehlermeldungen\n\nWenn Sie Docker verwenden und Probleme auftreten:\n\n1. Überprüfen Sie, ob der Container ausgeführt wird: `docker ps | grep gpt-mcp-server`\n2. Containerprotokolle prüfen: `docker logs gpt-mcp-server`\n3. Bestätigen Sie, dass der Server an alle Schnittstellen gebunden ist. Die Protokolle sollten ein Abhören auf 0.0.0.0:8000 anzeigen.\n4. Wenn während des Builds Abhängigkeitsfehler auftreten:\n * Stellen Sie sicher, dass Sie das aktualisierte Dockerfile mit Python 3.11 verwenden\n * Versuchen Sie einen Neuaufbau mit dem Flag `--no-cache` : `docker build --no-cache -t gpt-mcp-server .`\n * Überprüfen Sie, ob die Version von gpt-researcher in Ihrer Datei requirements.txt mit der Python-Version übereinstimmt.\n\nBei Problemen mit der lokalen Installation:\n\n```bash\n# Check your Python version (must be 3.11+)\npython --version\n\n# Update pip to the latest version\npip install --upgrade pip\n\n# If you're using a virtual environment, create a new one with Python 3.11\npython3.11 -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n```\n\n## 👣 Nächste Schritte\n\n* Erkunden Sie die [MCP-Protokolldokumentation,](https://docs.anthropic.com/claude/docs/model-context-protocol) um besser zu verstehen, wie Sie mit Claude integrieren können\n* Erfahren Sie mehr über [die Kernfunktionen von GPT Researcher](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction) zur Verbesserung Ihrer Forschungsmöglichkeiten\n* Weitere Konfigurationsoptionen finden Sie im Handbuch [„Erweiterte Nutzung“](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage)\n\n## 📄 Lizenz\n\nDieses Projekt ist unter der MIT-Lizenz lizenziert – Einzelheiten finden Sie in der Datei LICENSE.\n\n## 📞 Support / Kontakt\n\n* [Community-Discord](https://discord.gg/QgZXvJAccX)\n* E-Mail: ","German","tag","de-DE",{"_29":41,"_33":45,"_38":46},{"_31":42,"_33":43,"_35":44},"Enhances LLM applications with deep autonomous web research capabilities, delivering higher quality information than standard search tools by exploring and validating numerous trusted sources.","GPT Researcher MCP Server","

\n\n $\"Logo\"$ \n\n# 🔍 GPT Researcher MCP Server\n\n[![Website](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge&logo=world&logoColor=white&color=0891b2)](https://gptr.dev)\n[![Documentation](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs&logoColor=white&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started)\n[![Discord Follow](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n

\n\n## Why GPT Researcher MCP?\n\nWhile LLM apps can access web search tools with MCP, **GPT Researcher MCP delivers deep research results.** Standard search tools return raw results requiring manual filtering, often containing irrelevant sources and wasting context window space.\n\nGPT Researcher autonomously explores and validates numerous sources, focusing only on relevant, trusted and up-to-date information. Though slightly slower than standard search (~30 seconds wait), it delivers:\n\n- ✨ Higher quality information\n- 📊 Optimized context usage\n- 🔎 Comprehensive results\n- 🧠 Better reasoning for LLMs\n\n## 💻 Claude Desktop Demo\nhttps://github.com/user-attachments/assets/ef97eea5-a409-42b9-8f6d-b82ab16c52a8\n\n### Resources\n- `research_resource`: Get web resources related to a given task via research.\n\n### Primary Tools\n\n- `deep_research`: Performs deep web research on a topic, finding the most reliable and relevant information\n- `quick_search`: Performs a fast web search optimized for speed over quality, returning search results with snippets. Supports any GPTR supported web retriever such as Tavily, Bing, Google, etc... Learn more [here](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers)\n- `write_report`: Generate a report based on research results\n- `get_research_sources`: Get the sources used in the research\n- `get_research_context`: Get the full context of the research\n\n### Prompts\n\n- `research_query`: Create a research query prompt\n\n## Prerequisites\n\nBefore running the MCP server, make sure you have:\n\n1. Python 3.11 or higher installed\n - **Important**: GPT Researcher >=0.12.16 requires Python 3.11+\n2. API keys for the services you plan to use:\n - [OpenAI API key](https://platform.openai.com/api-keys)\n - [Tavily API key](https://app.tavily.com)\n\n## ⚙️ Installation\n\n1. Clone the GPT Researcher repository:\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. Install the gptr-mcp dependencies:\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. Set up your environment variables:\n - Copy the `.env.example` file to create a new file named `.env`:\n ```bash\n cp .env.example .env\n ```\n - Edit the `.env` file and add your API keys and configure other settings:\n ```bash\n OPENAI_API_KEY=your_openai_api_key\n TAVILY_API_KEY=your_tavily_api_key\n ```\nYou can also add any other env variable for your GPT Researcher configuration.\n\n## 🚀 Running the MCP Server\n\nYou can run the MCP server in several ways:\n\n### Method 1: Directly using Python\n\n```bash\npython server.py\n```\n\n### Method 2: Using the MCP CLI (if installed)\n\n```bash\nmcp run server.py\n```\n\n### Method 3: Using Docker (recommended for production)\n\n#### Option A: Standalone Mode\n\nThis is the simplest way to run the server if you don't need to connect to other containers:\n\n```bash\n# Build and run with docker-compose\ndocker-compose -f docker-compose.standalone.yml up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n#### Option B: With Network for n8n Integration\n\nIf you need to connect to other services like n8n on the same network:\n\n```bash\n# Create the network if it doesn't exist\ndocker network create n8n-mcp-net\n\n# Build and run with docker-compose\ndocker-compose up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n --network n8n-mcp-net \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n**Note**: The Docker image uses Python 3.11 to meet the requirements of gpt-researcher >=0.12.16. If you encounter errors during the build, ensure you're using the latest Dockerfile from this repository.\n\nOnce the server is running, you'll see output indicating that the server is ready to accept connections. You can verify it's working by:\n\n1. Accessing the OpenAPI docs at http://localhost:8000/docs\n2. Testing the MCP endpoint at http://localhost:8000/mcp\n\n## Integrating with Claude\n\nYou can integrate your MCP server with Claude using:\n\n**[Claude Desktop Integration](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)** - For using with Claude desktop application on Mac\n\nFor detailed instructions, follow the link above.\n\n## 💻 Claude Desktop Integration\n\nTo integrate your locally running MCP server with Claude for Mac, you'll need to:\n\n1. Make sure the MCP server is installed and running\n2. Configure Claude Desktop:\n - Locate or create the configuration file at `~/Library/Application Support/Claude/claude_desktop_config.json`\n - Add your local GPT Researcher MCP server to the configuration\n - Restart Claude to apply the configuration\n\nFor complete step-by-step instructions, see the [Claude Desktop Integration guide](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration).\n\n## 📝 Example Usage with Claude\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 Troubleshooting\n\nIf you encounter issues while running the MCP server:\n\n1. Make sure your API keys are correctly set in the `.env` file\n2. Check that you're using Python 3.11 or higher (required by gpt-researcher >=0.12.16)\n3. Ensure all dependencies are installed correctly\n4. Check the server logs for error messages\n\nIf you're running with Docker and experiencing issues:\n\n1. Verify the container is running: `docker ps | grep gpt-mcp-server`\n2. Check container logs: `docker logs gpt-mcp-server`\n3. Confirm the server is binding to all interfaces - logs should show listening on 0.0.0.0:8000\n4. If you see dependency errors during build:\n - Ensure you're using the updated Dockerfile with Python 3.11\n - Try rebuilding with the `--no-cache` flag: `docker build --no-cache -t gpt-mcp-server .`\n - Check the version of gpt-researcher in your requirements.txt matches the Python version\n\nFor local installation issues:\n```bash\n# Check your Python version (must be 3.11+)\npython --version\n\n# Update pip to the latest version\npip install --upgrade pip\n\n# If you're using a virtual environment, create a new one with Python 3.11\npython3.11 -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n```\n\n## 👣 Next Steps\n\n- Explore the [MCP protocol documentation](https://docs.anthropic.com/claude/docs/model-context-protocol) to better understand how to integrate with Claude\n- Learn about [GPT Researcher's core features](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction) to enhance your research capabilities\n- Check out the [Advanced Usage](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage) guide for more configuration options\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## 📞 Support / Contact\n\n- [Community Discord](https://discord.gg/QgZXvJAccX)\n- Email: assaf.elovic@gmail.com\n\n

\n ⬆️ Back to Top\n

\n","English","en-US",{"_29":48,"_33":52,"_38":53},{"_31":49,"_33":50,"_35":51},"Mejora las aplicaciones LLM con profundas capacidades de investigación web autónoma, brindando información de mayor calidad que las herramientas de búsqueda estándar al explorar y validar numerosas fuentes confiables.","Servidor MCP del investigador GPT","# Servidor MCP del investigador GPT\n\n[![Sitio web](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![Documentación](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![Seguir en Discord](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## ¿Por qué GPT Researcher MCP?\n\nMientras que las aplicaciones LLM pueden acceder a herramientas de búsqueda web con MCP, **GPT Researcher MCP ofrece resultados de investigación exhaustivos.** Las herramientas de búsqueda estándar devuelven resultados sin procesar que requieren filtrado manual, a menudo con fuentes irrelevantes y desperdiciando espacio en la ventana de contexto.\n\nGPT Researcher explora y valida numerosas fuentes de forma autónoma, centrándose únicamente en información relevante, fiable y actualizada. Aunque es un poco más lento que la búsqueda estándar (aproximadamente 30 segundos de espera), ofrece:\n\n* ✨ Información de mayor calidad\n* 📊 Uso optimizado del contexto\n* 🔎 Resultados completos\n* 🧠 Mejor razonamiento para los LLM\n\n## Demostración de escritorio de Claude\n\n\n\n### Recursos\n\n* `research_resource` : Obtener recursos web relacionados con una tarea determinada a través de la investigación.\n\n### Herramientas primarias\n\n* `deep_research` : realiza una investigación web profunda sobre un tema y encuentra la información más confiable y relevante\n* `quick_search` : Realiza una búsqueda web rápida, priorizando la velocidad sobre la calidad, y devuelve resultados con fragmentos. Compatible con cualquier recuperador web compatible con GPTR, como Tavily, Bing, Google, etc. Más información [aquí.](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers)\n* `write_report` : Generar un informe basado en los resultados de la investigación\n* `get_research_sources` : Obtener las fuentes utilizadas en la investigación\n* `get_research_context` : Obtenga el contexto completo de la investigación\n\n### Indicaciones\n\n* `research_query` : Crea una solicitud de consulta de investigación\n\n## Prerrequisitos\n\nAntes de ejecutar el servidor MCP, asegúrese de tener:\n\n1. Python 3.10 o superior instalado\n2. Claves API para los servicios que planea utilizar:\n * [Clave API de OpenAI](https://platform.openai.com/api-keys)\n * [Clave API de Tavily](https://app.tavily.com)\n\n## ⚙️ Instalación\n\n1. Clonar el repositorio de GPT Researcher:\n\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. Instalar las dependencias gptr-mcp:\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. Configure sus variables de entorno:\n * Copie el archivo `.env.example` para crear un nuevo archivo llamado `.env` : GXP3\n * Edite el archivo `.env` y agregue sus claves API y configure otros ajustes: GXP4\n\nTambién puede agregar cualquier otra variable de entorno para su configuración de GPT Researcher.\n\n## 🚀 Ejecución del servidor MCP\n\nPuede iniciar el servidor MCP de dos maneras:\n\n### Método 1: Usar Python directamente\n\n```bash\npython server.py\n```\n\n### Método 2: Uso de la CLI de MCP (si está instalada)\n\n```bash\nmcp run server.py\n```\n\nUna vez que el servidor esté en ejecución, verá un resultado que indica que el servidor está listo para aceptar conexiones.\n\n## Integración con Claude\n\nPuedes integrar tu servidor MCP con Claude usando:\n\n**[Integración de escritorio de Claude](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)** : para usar con la aplicación de escritorio de Claude en Mac\n\nPara obtener instrucciones detalladas, siga el enlace de arriba.\n\n## 💻 Integración de escritorio de Claude\n\nPara integrar su servidor MCP que se ejecuta localmente con Claude para Mac, necesitará:\n\n1. Asegúrese de que el servidor MCP esté instalado y en funcionamiento\n2. Configurar Claude Desktop:\n * Localice o cree el archivo de configuración en `~/Library/Application Support/Claude/claude_desktop_config.json`\n * Agregue su servidor MCP de GPT Researcher local a la configuración\n * Reinicie Claude para aplicar la configuración\n\nPara obtener instrucciones completas paso a paso, consulte la [guía de integración de Claude Desktop](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration) .\n\n## 📝 Ejemplo de uso con Claude\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 Solución de problemas\n\nSi encuentra problemas al ejecutar el servidor MCP:\n\n1. Asegúrese de que sus claves API estén configuradas correctamente en el archivo `.env`\n2. Comprueba que estás utilizando Python 3.10 o superior\n3. Asegúrese de que todas las dependencias estén instaladas correctamente\n4. Verifique los registros del servidor para ver si hay mensajes de error\n\n## 👣 Próximos pasos\n\n* Explore la [documentación del protocolo MCP](https://docs.anthropic.com/claude/docs/model-context-protocol) para comprender mejor cómo integrarse con Claude\n* Conozca [las características principales de GPT Researcher](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction) para mejorar sus capacidades de investigación\n* Consulte la guía [de uso avanzado](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage) para obtener más opciones de configuración\n\n## 📄 Licencia\n\nEste proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.\n\n## 📞 Soporte / Contacto\n\n* [Discord de la comunidad](https://discord.gg/QgZXvJAccX)\n* Correo electrónico: ","Spanish","es-ES",{"_29":55,"_33":59,"_38":60},{"_31":56,"_33":57,"_35":58},"高度な自律型 Web リサーチ機能により LLM アプリケーションを強化し、多数の信頼できるソースを探索および検証することで、標準の検索ツールよりも高品質の情報を提供します。","GPT研究者MCPサーバー","# 🔍 GPT研究者MCPサーバー\n\n[![Webサイト](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![ドキュメント](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![Discord フォロー](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## GPT Researcher MCP を選ぶ理由\n\nLLMアプリはMCPを使用してWeb検索ツールにアクセスできますが、 \\*\\*GPT Researcher MCPは詳細な調査結果を提供します。\\*\\*標準的な検索ツールは、手動でフィルタリングする必要がある生の検索結果を返すため、関連性のないソースが含まれることが多く、コンテキストウィンドウのスペースを無駄にしています。\n\nGPT Researcherは、多数の情報源を自律的に探索・検証し、関連性が高く、信頼性が高く、最新の情報のみに焦点を当てます。標準的な検索よりも若干遅い（約30秒の待機時間）ものの、以下の機能を提供します。\n\n* ✨ より質の高い情報\n* 📊 コンテキストの使用を最適化\n* 🔎 包括的な結果\n* 🧠 LLMのためのより良い推論\n\n## 💻 クロードデスクトップデモ\n\n\n\n### リソース\n\n* `research_resource` : リサーチを通じて、特定のタスクに関連する Web リソースを取得します。\n\n### 主なツール\n\n* `deep_research` : トピックに関するディープウェブリサーチを実行し、最も信頼性が高く関連性の高い情報を見つけます。\n* `quick_search` ：品質よりも速度を重視した高速ウェブ検索を実行し、スニペット付きの検索結果を返します。Tavily、Bing、Googleなど、GPTR対応のウェブリトリーバーをサポートしています。詳細は[こちらを](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers)ご覧ください。\n* `write_report` : 調査結果に基づいてレポートを生成する\n* `get_research_sources` : 研究で使用されたソースを取得する\n* `get_research_context` : 研究の完全なコンテキストを取得する\n\n### プロンプト\n\n* `research_query` : リサーチクエリプロンプトを作成する\n\n## 前提条件\n\nMCP サーバーを実行する前に、次のものを用意してください。\n\n1. Python 3.10以降がインストールされている\n2. 使用予定のサービスの API キー:\n * [OpenAI APIキー](https://platform.openai.com/api-keys)\n * [Tavily APIキー](https://app.tavily.com)\n\n## ⚙️ インストール\n\n1. GPT Researcher リポジトリをクローンします。\n\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. gptr-mcp の依存関係をインストールします。\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. 環境変数を設定します。\n * `.env.example`ファイルをコピーして、 `.env` : GXP3 という名前の新しいファイルを作成します。\n * `.env`ファイルを編集し、APIキ��を追加し、その他の設定を構成します: GXP4\n\nGPT Researcher 構成に他の環境変数を追加することもできます。\n\n## 🚀 MCP サーバーの実行\n\nMCP サーバーは 2 つの方法で起動できます。\n\n### 方法1: Pythonを直接使用する\n\n```bash\npython server.py\n```\n\n### 方法 2: MCP CLI を使用する (インストールされている場合)\n\n```bash\nmcp run server.py\n```\n\nサーバーが実行される場合は、サーバーが接続を受け入れる準備ができていることを示す出力が表示されます。\n\n## クロードとの統合\n\n以下の方法で MCP サーバーを Claude と統合できます。\n\n**[Claude デスクトップ統合](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)**- Mac 上の Claude デスクトップアプリケーションで使用する場合\n\n詳細な手順については、上記のリンクを参照してください。\n\n## 💻 クロードデスクトップ統合\n\nローカルで実行されている MCP サーバーを Claude for Mac と統合するには、次の操作を行う必要があります。\n\n1. MCPサーバーがインストールされ、実行されていることを確認してください\n2. Claude デスクトップを設定します。\n * `~/Library/Application Support/Claude/claude_desktop_config.json`で設定ファイルを探すか作成します。\n * ローカルのGPT Researcher MCPサーバーを構成に追加する\n * 設定を適用するにはClaudeを再起動してください\n\n詳細な手順については、 [Claude デスクトップ統合ガイドを](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)参照してください。\n\n## 📝 Claude の使用例\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 トラブルシューティング\n\nMCP サーバーの実行中に問題が発生した場合:\n\n1. `.env`ファイルにAPIキーが正しく設定されていることを確認してください\n2. Python 3.10以降を使用していることを確認してください\n3. すべての依存関係が正しくインストールされていることを確認する\n4. サーバーログでエラーメッセージを確認してください\n\n## 👣 次のステップ\n\n* Claudeとの統合方法をより深く理解するために、 [MCPプロトコルのドキュメント](https://docs.anthropic.com/claude/docs/model-context-protocol)をご覧ください。\n* [GPT Researcherのコア機能](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction)について学び、研究能力を高めましょう\n* 詳細な設定オプションについては、[高度な使用](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage)ガイドをご覧ください。\n\n## 📄 ライセンス\n\nこのプロジェクトは MIT ライセンスに基づいてライセンスされています - 詳細については LICENSE ファイルを参照してください。\n\n## 📞 サポート / お問い合わせ\n\n* [コミュニティの不和](https://discord.gg/QgZXvJAccX)\n* メールアドレス: ","Japanese","ja-JP",{"_29":62,"_33":66,"_38":67},{"_31":63,"_33":64,"_35":65},"수많은 신뢰할 수 있는 출처를 탐색하고 검증하여 표준 검색 도구보다 더 높은 품질의 정보를 제공하고, 심층적인 자율 웹 연구 기능으로 LLM 애플리케이션을 향상시킵니다.","GPT Researcher MCP 서버","# 🔍 GPT Researcher MCP 서버\n\n[![웹사이트](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![선적 서류 비치](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![디스코드 팔로우](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## 왜 GPT Researcher MCP인가요?\n\nLLM 앱은 MCP를 통해 웹 검색 도구에 액세스할 수 있지만, **GPT Researcher MCP는 심층적인 연구 결과를 제공합니다.** 표준 검색 도구는 수동 필터링이 필요한 원시 결과를 반환하며, 관련성 없는 출처를 포함하고 맥락 정보를 낭비하는 경우가 많습니다.\n\nGPT Researcher는 수많은 출처를 자율적으로 탐색하고 검증하며, 관련성 있고 신뢰할 수 있으며 최신 정보만을 중점적으로 다룹니다. 일반 검색보다 약간 느리지만(대기 시간 약 30초), 다음과 같은 기능을 제공합니다.\n\n* ✨ 더 높은 품질의 정보\n* 📊 최적화된 컨텍스트 사용\n* 🔎 종합적인 결과\n* 🧠 LLM에 대한 더 나은 추론\n\n## 💻 클로드 데스크톱 데모\n\n\n\n### 자원\n\n* `research_resource` : 연구를 통해 주어진 작업과 관련된 웹 리소스를 얻습니다.\n\n### 기본 도구\n\n* `deep_research` : 특정 주제에 대한 심층 웹 조사를 수행하여 가장 신뢰할 수 있고 관련성 있는 정보를 찾습니다.\n* `quick_search` : 품질보다 속도에 최적화된 빠른 웹 검색을 수행하여 검색 결과를 스니펫으로 반환합니다. Tavily, Bing, Google 등 GPTR을 지원하는 모든 웹 검색 도구를 지원합니다. 자세한 내용은 [여기를](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers) 참조하세요.\n* `write_report` : 조사 결과를 기반으로 보고서 생성\n* `get_research_sources` : 연구에 사용된 소스를 가져옵니다.\n* `get_research_context` : 연구의 전체 맥락을 얻습니다.\n\n### 프롬프트\n\n* `research_query` : 연구 쿼리 프롬프트를 만듭니다.\n\n## 필수 조건\n\nMCP 서버를 실행하기 전에 다음 사항을 확인하세요.\n\n1. Python 3.10 이상 설치됨\n2. 사용하려는 서비스에 대한 API 키:\n * [OpenAI API 키](https://platform.openai.com/api-keys)\n * [타빌리 API 키](https://app.tavily.com)\n\n## ⚙️ 설치\n\n1. GPT Researcher 저장소를 복제합니다.\n\n지엑스피1\n\n2. gptr-mcp 종속성을 설치합니다.\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. 환경 변수를 설정하세요.\n * `.env.example` 파일을 복사하여 `.env` 라는 이름의 새 파일을 만듭니다: GXP3\n * `.env` 파일을 편집하고 API 키를 추가하고 기타 설정을 구성하세요: GXP4\n\nGPT Researcher 구성에 다른 env 변수를 추가할 수도 있습니다.\n\n## 🚀 MCP 서버 실행\n\nMCP 서버는 두 가지 방법으로 시작할 수 있습니다.\n\n### 방법 1: Python을 직접 사용하기\n\n```bash\npython server.py\n```\n\n### 방법 2: MCP CLI 사용(설치된 경우)\n\n```bash\nmcp run server.py\n```\n\n서버가 실행되면 서버가 연결을 수락할 준비가 되었다는 출력이 표시됩니다.\n\n## Claude와 통합\n\n다음을 사용하여 MCP 서버를 Claude와 통합할 수 있습니다.\n\n**[Claude 데스크톱 통합](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)** - Mac에서 Claude 데스크톱 애플리케이션과 함께 사용\n\n자세한 지침은 위의 링크를 따라가세요.\n\n## 💻 Claude 데스크톱 통합\n\n로컬로 실행 중인 MCP 서버를 Claude for Mac과 통합하려면 다음이 필요합니다.\n\n1. MCP 서버가 설치되어 실행 중인지 확인하세요.\n2. Claude Desktop 구성:\n * `~/Library/Application Support/Claude/claude_desktop_config.json` 에서 구성 파일을 찾거나 만듭니다.\n * 구성에 로컬 GPT Researcher MCP 서버를 추가합니다.\n * 구성을 적용하려면 Claude를 다시 시작하세요.\n\n단계별 전체 지침은 [Claude Desktop 통합 가이드를](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration) 참조하세요.\n\n## 📝 Claude를 사용한 예시 사용\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 문제 해결\n\nMCP 서버를 실행하는 동안 문제가 발생하는 경우:\n\n1. `.env` 파일에 API 키가 올바르게 설정되어 있는지 확인하세요.\n2. Python 3.10 이상을 사용하고 있는지 확인하세요.\n3. 모든 종속성이 올바르게 설치되었는지 확인하세요\n4. 오류 메시지는 서버 로그에서 확인하세요.\n\n## 👣 다음 단계\n\n* Claude와 통합하는 방법을 더 잘 이해하려면 [MCP 프로토콜 문서를](https://docs.anthropic.com/claude/docs/model-context-protocol) 살펴보세요.\n* 귀하의 연구 역량을 강화하는 [GPT Researcher의 핵심 기능](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction) 에 대해 알아보세요\n* 더 많은 구성 옵션을 보려면 [고급 사용](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage) 가이드를 확인하세요.\n\n## 📄 라이센스\n\n이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다. 자세한 내용은 라이선스 파일을 참조하세요.\n\n## 📞 지원 / 문의\n\n* [커뮤니티 디스코드](https://discord.gg/QgZXvJAccX)\n* 이메일: ","Korean","ko-KR",{"_29":69,"_33":73,"_38":74},{"_31":70,"_33":71,"_35":72},"Расширяет возможности приложений LLM с помощью глубоких автономных возможностей веб-исследования, предоставляя более качественную информацию, чем стандартные инструменты поиска, путем изучения и проверки многочисленных надежных источников.","GPT Researcher MCP-сервер","# 🔍 Сервер MCP исследователя GPT\n\n[![Веб-сайт](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![Документация](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![Discord Подписаться](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## Почему GPT Researcher MCP?\n\nВ то время как приложения LLM могут получать доступ к инструментам веб-поиска с помощью MCP, **GPT Researcher MCP обеспечивает глубокие результаты исследований.** Стандартные инструменты поиска возвращают необработанные результаты, требующие ручной фильтрации, часто содержащие нерелевантные источники и тратящие пространство контекстного окна.\n\nGPT Researcher автономно исследует и проверяет многочисленные источники, фокусируясь только на релевантной, надежной и актуальной информации. Хотя он немного медленнее стандартного поиска (ожидание около 30 секунд), он обеспечивает:\n\n* ✨ Более качественная информация\n* 📊 Оптимизированное использование контекста\n* 🔎 Комплексные результаты\n* 🧠 Лучшее обоснование для LLM\n\n## 💻 Демонстрация рабочего стола Клода\n\n\n\n### Ресурсы\n\n* `research_resource` : Получить веб-ресурсы, связанные с заданной задачей, с помощью исследования.\n\n### Основные инструменты\n\n* `deep_research` : выполняет глубокое веб-исследование по теме, находя наиболее надежную и релевантную информацию.\n* `quick_search` : Выполняет быстрый поиск в Интернете, оптимизированный для скорости, а не качества, возвращая результаты поиска с фрагментами. Поддерживает любой поддерживаемый GPTR веб-извлекатель, такой как Tavily, Bing, Google и т. д... Узнайте больше [здесь](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers)\n* `write_report` : Создать отчет на основе результатов исследования\n* `get_research_sources` : Получить источники, использованные в исследовании\n* `get_research_context` : Получить полный контекст исследования\n\n### Подсказки\n\n* `research_query` : Создать запрос на исследовательский запрос\n\n## Предпосылки\n\nПеред запуском сервера MCP убедитесь, что у вас есть:\n\n1. Установлен Python 3.11 или выше\n * **Важно** : GPT Researcher >=0.12.16 требует Python 3.11+\n2. API-ключи для сервисов, которые вы планируете использовать:\n * [API-ключ OpenAI](https://platform.openai.com/api-keys)\n * [API-ключ Tavily](https://app.tavily.com)\n\n## ⚙️ Установка\n\n1. Клонируйте репозиторий GPT Researcher:\n\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. Установите зависимости gptr-mcp:\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. Настройте переменные среды:\n * Скопируйте файл `.env.example` , чтобы создать новый файл с именем `.env` : GXP3\n * Отредактируйте файл `.env` , добавьте свои ключи API и настройте другие параметры: GXP4\n\nВы также можете добавить любую другую переменную окружения для конфигурации GPT Researcher.\n\n## 🚀 Запуск сервера MCP\n\nЗапустить сервер MCP можно несколькими способами:\n\n### Метод 1: Непосредственное использование Python\n\n```bash\npython server.py\n```\n\n### Метод 2: Использование MCP CLI (если установлен)\n\n```bash\nmcp run server.py\n```\n\n### Метод 3: Использование Docker (рекомендуется для производства)\n\n#### Вариант А: Автономный режим\n\nЭто самый простой способ запустить сервер, если вам не нужно подключаться к другим контейнерам:\n\n```bash\n# Build and run with docker-compose\ndocker-compose -f docker-compose.standalone.yml up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n#### Вариант B: С сетью для интеграции n8n\n\nЕсли вам необходимо подключиться к другим сервисам, например n8n, в той же сети:\n\n```bash\n# Create the network if it doesn't exist\ndocker network create n8n-mcp-net\n\n# Build and run with docker-compose\ndocker-compose up -d\n\n# Or manually:\ndocker build -t gpt-mcp-server .\ndocker run -d \\\n --name gpt-mcp-server \\\n --network n8n-mcp-net \\\n -p 8000:8000 \\\n --env-file .env \\\n gpt-mcp-server\n```\n\n**Примечание** : Образ Docker использует Python 3.11 для соответствия требованиям gpt-researcher >=0.12.16. Если вы столкнулись с ошибками во время сборки, убедитесь, что вы используете последнюю версию Dockerfile из этого репозитория.\n\nПосле запуска сервера вы увидите вывод, указывающий на то, что сервер готов принимать соединения. Вы можете проверить его работу следующим образом:\n\n1. Доступ к документации OpenAPI по адресу \n2. Тестирование конечной точки MCP по адресу \n\n## Интеграция с Клодом\n\nВы можете интегрировать свой MCP-сервер с Claude, используя:\n\n**[Интеграция с Claude Desktop](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)** — для использования с приложением Claude Desktop на Mac\n\nПодробные инструкции можно найти по ссылке выше.\n\n## 💻 Интеграция с рабочим столом Клода\n\nЧтобы интегрировать локально работающий сервер MCP с Claude для Mac, вам необходимо:\n\n1. Убедитесь, что сервер MCP установлен и работает.\n2. Настройте рабочий стол Claude:\n * Найдите или создайте файл конфигурации в `~/Library/Application Support/Claude/claude_desktop_config.json`\n * Добавьте ваш локальный сервер GPT Researcher MCP в конфигурацию\n * Перезапустите Claude, чтобы применить конфигурацию.\n\nПолные пошаговые инструкции см. [в руководстве по интеграции Claude Desktop](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration) .\n\n## 📝 Пример использования с Клодом\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 Устранение неполадок\n\nЕсли у вас возникли проблемы при работе сервера MCP:\n\n1. Убедитесь, что ваши ключи API правильно установлены в файле `.env`\n2. Убедитесь, что вы используете Python 3.11 или выше (требуется для gpt-researcher >=0.12.16)\n3. Убедитесь, что все зависимости установлены правильно.\n4. Проверьте журналы сервера на наличие сообщений об ошибках.\n\nЕсли вы работаете с Docker и столкнулись с проблемами:\n\n1. Убедитесь, что контейнер запущен: `docker ps | grep gpt-mcp-server`\n2. Проверьте логи контейнера: `docker logs gpt-mcp-server`\n3. Убедитесь, что сервер привязан ко всем интерфейсам — журналы должны показывать прослушивание на 0.0.0.0:8000.\n4. Если во время сборки вы видите ошибки зависимостей:\n * Убедитесь, что вы используете обновленный Dockerfile с Python 3.11\n * Попробуйте выполнить пересборку с флагом `--no-cache` : `docker build --no-cache -t gpt-mcp-server .`\n * Проверьте, соответствует ли версия gpt-researcher в файле requirements.txt версии Python.\n\nПо вопросам локальной установки:\n\n```bash\n# Check your Python version (must be 3.11+)\npython --version\n\n# Update pip to the latest version\npip install --upgrade pip\n\n# If you're using a virtual environment, create a new one with Python 3.11\npython3.11 -m venv venv\nsource venv/bin/activate # On Windows: venv\\Scripts\\activate\n```\n\n## 👣 Дальнейшие шаги\n\n* Изучите [документацию по протоколу MCP,](https://docs.anthropic.com/claude/docs/model-context-protocol) чтобы лучше понять, как интегрироваться с Claude.\n* Узнайте об [основных функциях GPT Researcher,](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction) которые расширят ваши исследовательские возможности.\n* Ознакомьтесь с руководством [по расширенному использованию](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage) для получения дополнительных параметров конфигурации.\n\n## 📄 Лицензия\n\nДанный проект лицензирован по лицензии MIT — подробности см. в файле LICENSE.\n\n## 📞 Поддержка / Контакты\n\n* [Сообщество Discord](https://discord.gg/QgZXvJAccX)\n* Электронная почта: ","Russian","ru-RU",{"_29":76,"_33":80,"_38":81},{"_31":77,"_33":78,"_35":79},"通过深度自主网络研究功能增强 LLM 应用程序，通过探索和验证众多可信来源，提供比标准搜索工具更高质量的信息。","GPT 研究员 MCP 服务器","# 🔍 GPT 研究员 MCP 服务器\n\n[![网站](https://img.shields.io/badge/Official%20Website-gptr.dev-teal?style=for-the-badge\\&logo=world\\&logoColor=white\\&color=0891b2)](https://gptr.dev) [![文档](https://img.shields.io/badge/Documentation-DOCS-f472b6?logo=googledocs\\&logoColor=white\\&style=for-the-badge)](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/getting-started) [![Discord 关注](https://dcbadge.vercel.app/api/server/QgZXvJAccX?style=for-the-badge\\&theme=clean-inverted&?compact=true)](https://discord.gg/QgZXvJAccX)\n\n## 为什么选择 GPT Research MCP？\n\n虽然 LLM 应用可以通过 MCP 访问网络搜索工具，但\\*\\*GPT Researcher MCP 能够提供深入的研究结果。\\*\\*标准搜索工具返回的原始结果需要手动过滤，而且通常包含不相关的来源，浪费上下文窗口空间。\n\nGPT Researcher 会自主探索并验证众多来源，仅关注相关、可信且最新的信息。虽然速度比标准搜索略慢（约 30 秒等待时间），但它能够提供：\n\n* ✨ 更高质量的信息\n* 📊 优化上下文使用\n* 🔎 综合结果\n* 🧠 更好地推理法学硕士\n\n## 💻 Claude 桌面演示\n\n\n\n### 资源\n\n* `research_resource` ：通过研究获取与给定任务相关的网络资源。\n\n### 主要工具\n\n* `deep_research` ：对某个主题进行深度网络研究，找到最可靠和最相关的信息\n* `quick_search` ：执行快速网页搜索，优化速度而非质量，返回包含摘要的搜索结果。支持任何支持 GPTR 的网页检索工具，例如 Tavily、Bing、Google 等……点击[此处](https://docs.gptr.dev/docs/gpt-researcher/search-engines/retrievers)了解更多信息\n* `write_report` ：根据研究结果生成报告\n* `get_research_sources` ：获取研究中使用的来源\n* `get_research_context` ：获取研究的完整背景\n\n### 提示\n\n* `research_query` ：创建研究查询提示\n\n## 先决条件\n\n在运行 MCP 服务器之前，请确保您已：\n\n1. 已安装 Python 3.10 或更高版本\n2. 您计划使用的服务的 API 密钥：\n * [OpenAI API 密钥](https://platform.openai.com/api-keys)\n * [Tavily API 密钥](https://app.tavily.com)\n\n## ⚙️ 安装\n\n1. 克隆 GPT Researcher 存储库：\n\n```bash\ngit clone https://github.com/assafelovic/gpt-researcher.git\ncd gpt-researcher\n```\n\n2. 安装 gptr-mcp 依赖项：\n\n```bash\ncd gptr-mcp\npip install -r requirements.txt\n```\n\n3. 设置环境变量：\n * 复制`.env.example`文件以创建一个名为`.env`的新文件：GXP3\n * 编辑`.env`文件并添加您的 API 密钥并配置其他设置：GXP4\n\n您还可以为 GPT Researcher 配置添加任何其他环境变量。\n\n## 🚀 运行 MCP 服务器\n\n您可以通过两种方式启动 MCP 服务器：\n\n### 方法一：直接使用Python\n\n```bash\npython server.py\n```\n\n### 方法 2：使用 MCP CLI（如果已安装）\n\n```bash\nmcp run server.py\n```\n\n一旦服务器运行，您将看到表明服务器已准备好接受连接的输出。\n\n## 与克劳德整合\n\n您可以使用以下方式将您的 MCP 服务器与 Claude 集成：\n\n**[Claude 桌面集成](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)**- 与 Mac 上的 Claude 桌面应用程序一起使用\n\n欲了解详细说明，请点击上面的链接。\n\n## 💻 Claude 桌面集成\n\n要将本地运行的 MCP 服务器与 Claude for Mac 集成，您需要：\n\n1. 确保 MCP 服务器已安装并正在运行\n2. 配置Claude桌面：\n * 在`~/Library/Application Support/Claude/claude_desktop_config.json`找到或创建配置文件\n * 将本地 GPT Researcher MCP 服务器添加到配置中\n * 重启 Claude 以应用配置\n\n有关完整的分步说明，请参阅[Claude Desktop Integration 指南](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/claude-integration)。\n\n## 📝 Claude 的使用示例\n\n```\nUser: I'm considering investing in NVIDIA. Can you research the current state of the company, recent stock performance, latest product announcements, and analyst opinions?\n\nClaude: I'll help you research NVIDIA for investment purposes. This will take a moment as I conduct comprehensive research to find the most up-to-date information about their stock performance, recent developments, and expert analysis.\n\n[Claude uses conduct_research tool - this takes 30-40 seconds]\n\nClaude: Based on my research, here's a comprehensive analysis of NVIDIA's current state:\n\n## NVIDIA (NVDA) Current Status\n\n### Recent Stock Performance\n- Current price: $942.89 (as of my research)\n- YTD performance: +90.4% \n- 52-week range: $460.30 - $974.00\n- Market cap: ~$2.32 trillion\n\n### Recent Key Developments\n1. **Blackwell AI Platform**: At GTC 2024, NVIDIA unveiled the Blackwell architecture, their next-generation AI chip platform promising 4x performance improvement over Hopper. Major cloud providers (AWS, Google, Microsoft) have already committed to integrating Blackwell.\n\n2. **Q1 FY2025 Earnings (May 2024)**: \n - Revenue: $26.0 billion (+262% YoY)\n - Data Center revenue: $22.6 billion (+427% YoY)\n - Gross margin: 78.4%\n - Announced 10-for-1 stock split effective June 7, 2024\n\n3. **Supply Chain Expansion**: Increased investments in manufacturing capacity with TSMC to address ongoing chip shortages for their high-demand AI GPUs.\n\n### Analyst Consensus\n- Strong Buy consensus with 37 analysts rating it \"Buy,\" 5 \"Hold,\" and 0 \"Sell\"\n- Average price target: $1,042 (post-split adjusted: $104.20)\n- Key bull thesis: Continued AI infrastructure spending and NVIDIA's dominant position in AI chips\n- Key bear concerns: Valuation, potential competition from AMD, Intel, and custom silicon from tech giants\n\n### Industry Position\n- Dominant in AI GPU market with estimated 80-90% market share\n- Expanding ecosystem through CUDA software platform and industry partnerships\n- Diversifying beyond chips with DGX Cloud, AI software platforms, and automotive solutions\n```\n\n## 🔧 故障排除\n\n如果您在运行 MCP 服务器时遇到问题：\n\n1. 确保你的 API 密钥在`.env`文件中正确设置\n2. 检查您是否正在使用 Python 3.10 或更高版本\n3. 确保所有依赖项都正确安装\n4. 检查服务器日志中的错误消息\n\n## 👣 后续步骤\n\n* 探索[MCP 协议文档，](https://docs.anthropic.com/claude/docs/model-context-protocol)以更好地了解如何与 Claude 集成\n* 了解[GPT Researcher 的核心功能](https://docs.gptr.dev/docs/gpt-researcher/getting-started/introduction)，提升您的研究能力\n* 查看[高级使用](https://docs.gptr.dev/docs/gpt-researcher/mcp-server/advanced-usage)指南以获取更多配置选项\n\n## 📄 许可证\n\n该项目根据 MIT 许可证获得许可 - 有关详细信息，请参阅 LICENSE 文件。\n\n## 📞 支持/联系方式\n\n* [社区 Discord](https://discord.gg/QgZXvJAccX)\n* 邮箱: 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