{"info":{"author":null,"author_email":"\"mcp-use, Inc.\" , Pietro Zullo ","bugtrack_url":null,"classifiers":["Development Status :: 3 - Alpha","Intended Audience :: Developers","License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Topic :: Software Development :: Libraries :: Python Modules"],"description":"
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šŸš€ mcp-use for Python

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\n\n> **šŸ“¦ Part of the [mcp-use Monorepo](../../README.md)** - This is the Python implementation. Also available in [TypeScript](../typescript/README.md).\n\nšŸŒ **mcp-use for Python** is the complete way to connect **any LLM to any MCP server** and build custom MCP agents with tool access.\n\nšŸ’” Let your Python applications leverage the power of the Model Context Protocol with support for agents, clients, and advanced features.\n\n## šŸ—ļø What's Included\n\nmcp-use for Python provides three main capabilities:\n\n- **šŸ¤– MCP Agent** - Build AI agents that can use tools and reason across multiple steps\n- **šŸ”Œ MCP Client** - Connect directly to MCP servers for programmatic tool access\n- **šŸ› ļø MCP Server** - Create, Test and Deploy MCP servers.\n\n---\n\n## šŸ“– Quick Links\n\n- **[Main Repository](../../README.md)** - Overview of the entire mcp-use ecosystem\n- **[TypeScript Version](../typescript/README.md)** - TypeScript implementation with server framework\n- **[Documentation](https://mcp-use.com/docs)** - Complete online documentation\n- **[Examples](./examples/)** - Python code examples\n\n[![CI](https://img.shields.io/github/actions/workflow/status/mcp-use/mcp-use/ci.yml?branch=main&label=CI&style=flat)](https://github.com/mcp-use/mcp-use/actions/workflows/ci.yml)\n\n| Supports | |\n| :------------- | :-------------------------------------------------------------------- |\n| **Primitives** | Tools, Resources, Prompts, Sampling, Elicitation, Roots, Authentication |\n| **Transports** | Stdio, SSE, Streamable HTTP |\n\n## Features\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FeatureDescription
šŸ”„ Ease of useCreate your first MCP capable agent you need only 6 lines of code
šŸ¤– LLM FlexibilityWorks with any langchain supported LLM that supports tool calling (OpenAI, Anthropic, Groq, LLama etc.)
šŸŒ Code BuilderExplore MCP capabilities and generate starter code with the interactive code builder.
šŸ”— HTTP SupportDirect connection to MCP servers running on specific HTTP ports
āš™ļø Dynamic Server SelectionAgents can dynamically choose the most appropriate MCP server for a given task from the available pool
šŸ§© Multi-Server SupportUse multiple MCP servers simultaneously in a single agent
šŸ›”ļø Tool RestrictionsRestrict potentially dangerous tools like file system or network access
šŸ”§ Custom AgentsBuild your own agents with any framework using the LangChain adapter or create new adapters
ā“ What should we build nextLet us know what you'd like us to build next
\n\n---\n\n# šŸ¤– MCP Agent\n\nThe **MCP Agent** is an AI-powered agent that can use tools from MCP servers to accomplish complex tasks. It reasons across multiple steps, selecting and executing tools as needed.\n\n## Quick Start\n\nWith pip:\n\n```bash\npip install mcp-use\n```\n\nOr install from source:\n\n```bash\ngit clone https://github.com/mcp-use/mcp-use.git\ncd mcp-use\npip install -e .\n```\n\n### Installing LangChain Providers\n\nmcp_use works with various LLM providers through LangChain. You'll need to install the appropriate LangChain provider package for your chosen LLM. For example:\n\n```bash\n# For OpenAI\npip install langchain-openai\n\n# For Anthropic\npip install langchain-anthropic\n```\n\nFor other providers, check the [LangChain chat models documentation](https://python.langchain.com/docs/integrations/chat/) and add your API keys for the provider you want to use to your `.env` file.\n\n```bash\nOPENAI_API_KEY=\nANTHROPIC_API_KEY=\n```\n\n> **Important**: Only models with tool calling capabilities can be used with mcp_use. Make sure your chosen model supports function calling or tool use.\n\n### Spin up your agent:\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def main():\n # Load environment variables\n load_dotenv()\n\n # Create configuration dictionary\n config = {\n \"mcpServers\": {\"playwright\": {\"command\": \"npx\", \"args\": [\"@playwright/mcp@latest\"], \"env\": {\"DISPLAY\": \":1\"}}}\n }\n\n # Create MCPClient from configuration dictionary\n client = MCPClient.from_dict(config)\n\n # Create LLM\n llm = ChatOpenAI(model=\"gpt-4o\")\n\n # Create agent with the client\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n # Run the query\n result = await agent.run(\n \"Find the best restaurant in San Francisco\",\n )\n print(f\"\\nResult: {result}\")\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\nYou can also add the servers configuration from a config file like this:\n\n```python\nclient = MCPClient.from_config_file(os.path.join(\"browser_mcp.json\"))\n```\n\nExample configuration file (`browser_mcp.json`):\n\n```json\n{\n \"mcpServers\": {\n \"playwright\": {\n \"command\": \"npx\",\n \"args\": [\"@playwright/mcp@latest\"],\n \"env\": {\n \"DISPLAY\": \":1\"\n }\n }\n }\n}\n```\n\nFor other settings, models, and more, check out the documentation.\n\n## Streaming Agent Output\n\nmcp-use supports asynchronous streaming of agent output using the `stream` method on `MCPAgent`. This allows you to receive incremental results, tool actions, and intermediate steps as they are generated by the agent, enabling real-time feedback and progress reporting.\n\n### How to use\n\nCall `agent.stream(query)` and iterate over the results asynchronously:\n\n```python\nasync for chunk in agent.stream(\"Find the best restaurant in San Francisco\"):\n print(chunk[\"messages\"], end=\"\", flush=True)\n```\n\nEach chunk is a dictionary containing keys such as `actions`, `steps`, `messages`, and (on the last chunk) `output`. This enables you to build responsive UIs or log agent progress in real time.\n\n#### Example: Streaming in Practice\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def main():\n load_dotenv()\n client = MCPClient.from_config_file(\"browser_mcp.json\")\n llm = ChatOpenAI(model=\"gpt-4o\")\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n async for chunk in agent.stream(\"Look for job at nvidia for machine learning engineer.\"):\n print(chunk[\"messages\"], end=\"\", flush=True)\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\nThis streaming interface is ideal for applications that require real-time updates, such as chatbots, dashboards, or interactive notebooks.\n\n# Example Use Cases\n\n## Web Browsing with Playwright\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def main():\n # Load environment variables\n load_dotenv()\n\n # Create MCPClient from config file\n client = MCPClient.from_config_file(os.path.join(os.path.dirname(__file__), \"browser_mcp.json\"))\n\n # Create LLM\n llm = ChatOpenAI(model=\"gpt-4o\")\n # Alternative models:\n # llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n # llm = ChatGroq(model=\"llama3-8b-8192\")\n\n # Create agent with the client\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n # Run the query\n result = await agent.run(\n \"Find the best restaurant in San Francisco USING GOOGLE SEARCH\",\n max_steps=30,\n )\n print(f\"\\nResult: {result}\")\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n## Airbnb Search\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_anthropic import ChatAnthropic\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def run_airbnb_example():\n # Load environment variables\n load_dotenv()\n\n # Create MCPClient with Airbnb configuration\n client = MCPClient.from_config_file(os.path.join(os.path.dirname(__file__), \"airbnb_mcp.json\"))\n\n # Create LLM - you can choose between different models\n llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n\n # Create agent with the client\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n try:\n # Run a query to search for accommodations\n result = await agent.run(\n \"Find me a nice place to stay in Barcelona for 2 adults \"\n \"for a week in August. I prefer places with a pool and \"\n \"good reviews. Show me the top 3 options.\",\n max_steps=30,\n )\n print(f\"\\nResult: {result}\")\n finally:\n # Ensure we clean up resources properly\n if client.sessions:\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(run_airbnb_example())\n```\n\nExample configuration file (`airbnb_mcp.json`):\n\n```json\n{\n \"mcpServers\": {\n \"airbnb\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@openbnb/mcp-server-airbnb\"]\n }\n }\n}\n```\n\n## Blender 3D Creation\n\n```python\nimport asyncio\nfrom dotenv import load_dotenv\nfrom langchain_anthropic import ChatAnthropic\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def run_blender_example():\n # Load environment variables\n load_dotenv()\n\n # Create MCPClient with Blender MCP configuration\n config = {\"mcpServers\": {\"blender\": {\"command\": \"uvx\", \"args\": [\"blender-mcp\"]}}}\n client = MCPClient.from_dict(config)\n\n # Create LLM\n llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n\n # Create agent with the client\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n try:\n # Run the query\n result = await agent.run(\n \"Create an inflatable cube with soft material and a plane as ground.\",\n max_steps=30,\n )\n print(f\"\\nResult: {result}\")\n finally:\n # Ensure we clean up resources properly\n if client.sessions:\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(run_blender_example())\n```\n\n# Configuration Support\n\n## HTTP Connection Example\n\nmcp-use supports HTTP connections, allowing you to connect to MCP servers running on specific HTTP ports. This feature is particularly useful for integrating with web-based MCP servers.\n\nHere's an example of how to use the HTTP connection feature:\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use import MCPAgent, MCPClient\n\n\nasync def main():\n \"\"\"Run the example using a configuration file.\"\"\"\n # Load environment variables\n load_dotenv()\n\n config = {\"mcpServers\": {\"http\": {\"url\": \"http://localhost:8931/sse\"}}}\n\n # Create MCPClient from config file\n client = MCPClient.from_dict(config)\n\n # Create LLM\n llm = ChatOpenAI(model=\"gpt-4o\")\n\n # Create agent with the client\n agent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n # Run the query\n result = await agent.run(\n \"Find the best restaurant in San Francisco USING GOOGLE SEARCH\",\n max_steps=30,\n )\n print(f\"\\nResult: {result}\")\n\n\nif __name__ == \"__main__\":\n # Run the appropriate example\n asyncio.run(main())\n```\n\nThis example demonstrates how to connect to an MCP server running on a specific HTTP port. Make sure to start your MCP server before running this example.\n\n# Multi-Server Support\n\nmcp-use allows configuring and connecting to multiple MCP servers simultaneously using the `MCPClient`. This enables complex workflows that require tools from different servers, such as web browsing combined with file operations or 3D modeling.\n\n## Configuration\n\nYou can configure multiple servers in your configuration file:\n\n```json\n{\n \"mcpServers\": {\n \"airbnb\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@openbnb/mcp-server-airbnb\", \"--ignore-robots-txt\"]\n },\n \"playwright\": {\n \"command\": \"npx\",\n \"args\": [\"@playwright/mcp@latest\"],\n \"env\": {\n \"DISPLAY\": \":1\"\n }\n }\n }\n}\n```\n\n## Usage\n\nThe `MCPClient` class provides methods for managing connections to multiple servers. When creating an `MCPAgent`, you can provide an `MCPClient` configured with multiple servers.\n\nBy default, the agent will have access to tools from all configured servers. If you need to target a specific server for a particular task, you can specify the `server_name` when calling the `agent.run()` method.\n\n```python\n# Example: Manually selecting a server for a specific task\nresult = await agent.run(\n \"Search for Airbnb listings in Barcelona\",\n server_name=\"airbnb\", # Explicitly use the airbnb server\n)\n\nresult_google = await agent.run(\n \"Find restaurants near the first result using Google Search\",\n server_name=\"playwright\", # Explicitly use the playwright server\n)\n```\n\n## Dynamic Server Selection (Server Manager)\n\nFor enhanced efficiency and to reduce potential agent confusion when dealing with many tools from different servers, you can enable the Server Manager by setting `use_server_manager=True` during `MCPAgent` initialization.\n\nWhen enabled, the agent intelligently selects the correct MCP server based on the tool chosen by the LLM for a specific step. This minimizes unnecessary connections and ensures the agent uses the appropriate tools for the task.\n\n```python\nimport asyncio\nfrom mcp_use import MCPClient, MCPAgent\nfrom langchain_anthropic import ChatAnthropic\n\n\nasync def main():\n # Create client with multiple servers\n client = MCPClient.from_config_file(\"multi_server_config.json\")\n\n # Create agent with the client\n agent = MCPAgent(\n llm=ChatAnthropic(model=\"claude-3-5-sonnet-20240620\"),\n client=client,\n use_server_manager=True, # Enable the Server Manager\n )\n\n try:\n # Run a query that uses tools from multiple servers\n result = await agent.run(\n \"Search for a nice place to stay in Barcelona on Airbnb, \"\n \"then use Google to find nearby restaurants and attractions.\"\n )\n print(result)\n finally:\n # Clean up all sessions\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n# Tool Access Control\n\nmcp-use allows you to restrict which tools are available to the agent, providing better security and control over agent capabilities:\n\n```python\nimport asyncio\nfrom mcp_use import MCPAgent, MCPClient\nfrom langchain_openai import ChatOpenAI\n\n\nasync def main():\n # Create client\n client = MCPClient.from_config_file(\"config.json\")\n\n # Create agent with restricted tools\n agent = MCPAgent(\n llm=ChatOpenAI(model=\"gpt-4\"),\n client=client,\n disallowed_tools=[\"file_system\", \"network\"], # Restrict potentially dangerous tools\n )\n\n # Run a query with restricted tool access\n result = await agent.run(\"Find the best restaurant in San Francisco\")\n print(result)\n\n # Clean up\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n# Sandboxed Execution\n\nmcp-use supports running MCP servers in a sandboxed environment using E2B's cloud infrastructure. This allows you to run MCP servers without having to install dependencies locally, making it easier to use tools that might have complex setups or system requirements.\n\n## Installation\n\nTo use sandboxed execution, you need to install the E2B dependency:\n\n```bash\n# Install mcp-use with E2B support\npip install \"mcp-use[e2b]\"\n\n# Or install the dependency directly\npip install e2b-code-interpreter\n```\n\nYou'll also need an E2B API key. You can sign up at [e2b.dev](https://e2b.dev) to get your API key.\n\n## Configuration\n\nTo enable sandboxed execution, use the sandbox parameter when creating your `MCPClient`:\n\n```python\nimport asyncio\nimport os\nfrom dotenv import load_dotenv\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use import MCPAgent, MCPClient\nfrom mcp_use.types.sandbox import SandboxOptions\n\n\nasync def main():\n # Load environment variables (needs E2B_API_KEY)\n load_dotenv()\n\n # Define MCP server configuration\n server_config = {\n \"mcpServers\": {\n \"everything\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@modelcontextprotocol/server-everything\"],\n }\n }\n }\n\n # Define sandbox options\n sandbox_options: SandboxOptions = {\n \"api_key\": os.getenv(\"E2B_API_KEY\"), # API key can also be provided directly\n \"sandbox_template_id\": \"base\", # Use base template\n }\n\n # Create client with sandboxed mode enabled\n client = MCPClient(\n config=server_config,\n sandbox=True,\n sandbox_options=sandbox_options,\n )\n\n # Create agent with the sandboxed client\n llm = ChatOpenAI(model=\"gpt-4o\")\n agent = MCPAgent(llm=llm, client=client)\n\n # Run your agent\n result = await agent.run(\"Use the command line tools to help me add 1+1\")\n print(result)\n\n # Clean up\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n## Sandbox Options\n\nThe `SandboxOptions` type provides configuration for the sandbox environment:\n\n| Option | Description | Default |\n| ---------------------- | ---------------------------------------------------------------------------------------- | --------------------- |\n| `api_key` | E2B API key. Required - can be provided directly or via E2B_API_KEY environment variable | None |\n| `sandbox_template_id` | Template ID for the sandbox environment | \"base\" |\n| `supergateway_command` | Command to run supergateway | \"npx -y supergateway\" |\n\n## Benefits of Sandboxed Execution\n\n- **No local dependencies**: Run MCP servers without installing dependencies locally\n- **Isolation**: Execute code in a secure, isolated environment\n- **Consistent environment**: Ensure consistent behavior across different systems\n- **Resource efficiency**: Offload resource-intensive tasks to cloud infrastructure\n\n---\n\n# šŸ”Œ MCP Client\n\nThe **MCP Client** allows you to connect directly to MCP servers and call tools programmatically without an AI agent. This is useful when you know exactly which tools to call and don't need AI reasoning.\n\n## Direct Tool Calls (Without LLM)\n\nYou can call MCP server tools directly without an LLM when you need programmatic control:\n\n```python\nimport asyncio\nfrom mcp_use import MCPClient\n\n\nasync def call_tool_example():\n config = {\n \"mcpServers\": {\n \"everything\": {\n \"command\": \"npx\",\n \"args\": [\"-y\", \"@modelcontextprotocol/server-everything\"],\n }\n }\n }\n\n client = MCPClient.from_dict(config)\n\n try:\n await client.create_all_sessions()\n session = client.get_session(\"everything\")\n\n # Call tool directly\n result = await session.call_tool(name=\"add\", arguments={\"a\": 1, \"b\": 2})\n\n print(f\"Result: {result.content[0].text}\") # Output: 3\n\n finally:\n await client.close_all_sessions()\n\n\nif __name__ == \"__main__\":\n asyncio.run(call_tool_example())\n```\n\nSee the complete example: [examples/direct_tool_call.py](examples/direct_tool_call.py)\n\n# Build a Custom Agent:\n\nYou can also build your own custom agent using the LangChain adapter:\n\n```python\nimport asyncio\nfrom langchain_openai import ChatOpenAI\nfrom mcp_use.client import MCPClient\nfrom mcp_use.adapters.langchain_adapter import LangChainAdapter\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\nasync def main():\n # Initialize MCP client\n client = MCPClient.from_config_file(\"examples/browser_mcp.json\")\n llm = ChatOpenAI(model=\"gpt-4o\")\n\n # Create adapter instance\n adapter = LangChainAdapter()\n # Get LangChain tools with a single line\n tools = await adapter.create_tools(client)\n\n # Create a custom LangChain agent\n llm_with_tools = llm.bind_tools(tools)\n result = await llm_with_tools.ainvoke(\"What tools do you have available ? \")\n print(result)\n\n\nif __name__ == \"__main__\":\n asyncio.run(main())\n```\n\n---\n\n# šŸ› ļø MCP Server\n\nBuild MCP servers in Python with a simple decorator-based API:\n\n```python\nfrom mcp_use import MCPServer\n\nserver = MCPServer(\n name=\"my-server\",\n version=\"1.0.0\",\n)\n\n\n@server.tool()\ndef get_weather(city: str) -> dict:\n \"\"\"Get weather for a city\"\"\"\n return {\"temperature\": 72, \"condition\": \"sunny\", \"city\": city}\n\n\nif __name__ == \"__main__\":\n server.run()\n```\n\nFor the full server framework with UI widgets and built-in inspector, see the [TypeScript implementation](../typescript/README.md#%EF%B8%8F-mcp-server-framework).\n\n---\n\n# Debugging\n\nmcp-use provides a built-in debug mode that increases log verbosity and helps diagnose issues in your agent implementation.\n\n## Enabling Debug Mode\n\nThere are two primary ways to enable debug mode:\n\n### 1. Environment Variable (Recommended for One-off Runs)\n\nRun your script with the `DEBUG` environment variable set to the desired level:\n\n```bash\n# Level 1: Show INFO level messages\nDEBUG=1 python3.11 examples/browser_use.py\n\n# Level 2: Show DEBUG level messages (full verbose output)\nDEBUG=2 python3.11 examples/browser_use.py\n```\n\nThis sets the debug level only for the duration of that specific Python process.\n\nAlternatively you can set the following environment variable to the desired logging level:\n\n```bash\nexport MCP_USE_DEBUG=1 # or 2\n```\n\n### 2. Setting the Debug Flag Programmatically\n\nYou can set the global debug flag directly in your code:\n\n```python\nimport mcp_use\n\nmcp_use.set_debug(1) # INFO level\n# or\nmcp_use.set_debug(2) # DEBUG level (full verbose output)\n```\n\n### 3. Agent-Specific Verbosity\n\nIf you only want to see debug information from the agent without enabling full debug logging, you can set the `verbose` parameter when creating an MCPAgent:\n\n```python\n# Create agent with increased verbosity\nagent = MCPAgent(\n llm=your_llm,\n client=your_client,\n verbose=True, # Only shows debug messages from the agent\n)\n```\n\nThis is useful when you only need to see the agent's steps and decision-making process without all the low-level debug information from other components.\n\n## Star History\n\n[![Star History Chart](https://api.star-history.com/svg?repos=mcp-use/mcp-use&type=Date)](https://www.star-history.com/#mcp-use/mcp-use&Date)\n\n# Contributing\n\nWe love contributions! Feel free to open issues for bugs or feature requests. Look at [CONTRIBUTING.md](../../CONTRIBUTING.md) for guidelines.\n\n## Top Starred Dependents\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RepositoryStars
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krishnaik06/MCP-CRASH-Courseā­ 74
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\n\n\n\n# Requirements\n\n- Python 3.11+\n- MCP implementation (like Playwright MCP)\n- LangChain and appropriate model libraries (OpenAI, Anthropic, etc.)\n\n# License\n\nMIT\n\n# Citation\n\nIf you use mcp-use in your research or project, please cite:\n\n```bibtex\n@software{mcp_use2025,\n author = {Zullo, Pietro},\n title = {mcp-use: MCP Library for Python},\n year = {2025},\n publisher = {GitHub},\n url = 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