mcp-use
mcp-use is an open-source SDK and cloud platform that simplifies building and deploying MCP (Model Context Protocol) agents by providing a single endpoint to spin up, aggregate, and manage MCP servers with zero friction.
https://mcp-use.com/?ref=producthunt
Product Information
Updated:Aug 16, 2025
What is mcp-use
mcp-use is a comprehensive solution that bridges the gap between AI models and external tools/services through the Model Context Protocol (MCP). It offers both open-source libraries (available for Python and TypeScript) and a managed cloud platform that handles MCP server deployment, routing, authentication, and monitoring. The platform is trusted by major companies like IBM, NVIDIA, Oracle, and others, making it easier for developers to build AI applications that can seamlessly interact with various data sources and tools.
Key Features of mcp-use
mcp-use is an open source library and cloud platform that simplifies the integration of MCP (Model Context Protocol) servers with AI applications. It provides a unified gateway for managing multiple MCP servers, offering features like authentication, routing, monitoring, and deployment options including hosted, ephemeral, or on-premises servers. The platform enables developers to easily connect any LLM to MCP servers and build custom agents without relying on closed-source solutions.
Unified Gateway Management: Provides a single endpoint to route, authenticate, and load-balance all MCP servers with built-in OAuth, ACLs, metrics, and tracing capabilities
Flexible Deployment Options: Supports multiple deployment models including fully managed cloud servers, sandboxed local VMs, and third-party server integration
Simple Agent Creation: Enables creation of AI agents in just a few lines of code with automatic configuration and result streaming
Built-in Security Features: Includes comprehensive security features with authentication, authorization, and secure server routing
Use Cases of mcp-use
Enterprise Tool Integration: Large companies like IBM, NVIDIA, and Oracle use mcp-use to integrate their internal tools and data sources with AI models
Development Environment Enhancement: Integration with development tools and IDEs to provide AI-assisted coding and documentation capabilities
Data Source Connection: Connecting AI models to various data sources like Google Drive, Slack, and custom databases for enhanced context and functionality
Pros
Easy implementation with minimal setup required
Comprehensive security and monitoring features
Flexible deployment options to suit different needs
Cons
Dependency on AI model capabilities
Still an evolving ecosystem with potential stability concerns
How to Use mcp-use
Install mcp-use: Install the library using pip for Python (pip install mcp-use) or npm for TypeScript/JavaScript (npm install mcp-use)
Set up environment: Load environment variables using dotenv and ensure you have Python 3.10+ installed and required API keys configured
Create MCP configuration: Create a configuration dictionary defining your MCP servers with necessary parameters like command, args, and environment variables
Initialize MCPClient: Create an MCPClient instance using MCPClient.from_dict(config) with your configuration
Set up LLM: Initialize your chosen LLM (e.g., OpenAI, Anthropic, Groq, etc.) that supports function calling
Create MCPAgent: Initialize an MCPAgent with your LLM and MCPClient, specifying parameters like max_steps
Run queries: Use the agent.run() or agent.astream() method to execute queries and receive results, with astream providing real-time feedback
Handle tool permissions: When tools are invoked, approve their usage through the Allow dropdown options for the current session or future use
Monitor and debug: Use logging (not print statements) for debugging and monitoring tool execution and server responses
Scale deployment: Optionally deploy to cloud services like Cloudflare for remote access, or use the mcp-use cloud platform for managed hosting
mcp-use FAQs
MCP-use is a library and cloud platform that helps build and deploy MCP (Model Context Protocol) agents. It standardizes how applications provide context to LLMs, similar to how USB-C provides a standardized way to connect devices.
mcp-use Video
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