EasyMCP
EasyMCP is a comprehensive Python library designed for efficient management of multiple Model Context Protocol (MCP) servers. It provides features such as caching, dynamic server management, and namespaced tools and resources, enabling fast and scalable interactions with MCP servers for AI and data applications.
Added on:
Created by:
Apr 08 2025
EasyMCP is a comprehensive Python library designed for efficient management of multiple Model Context Protocol (MCP) servers. It provides features such as caching, dynamic server management, and namespaced tools and resources, enabling fast and scalable interactions with MCP servers for AI and data applications.
Added on:
Created by:
Apr 08 2025
Prompt Mesh
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What is EasyMCP?
EasyMCP is a high-performance SDK tailored for Python developers to manage multiple MCP servers seamlessly. It offers a ClientManager class for server lifecycle control, enabling addition, removal, and listing of servers. It supports parallel requests, out-of-the-box caching with invalidation, and namespaced tools and resources for rapid lookup and routing. This makes it ideal for building scalable AI/ML systems, data workflows, and integrations requiring efficient server management, resource access, and communication. Its asyncio-native design ensures lightweight and high concurrency, suitable for enterprise-grade applications that need quick interactions with MCP servers across distributed architectures.
Who will use EasyMCP?
- Python developers
- AI/ML engineers
- Data engineers
- System integrators
- Researchers working with MCP servers
How to use the EasyMCP?
- Step1: Install EasyMCP via pip
- Step2: Import ClientManager from easymcp
- Step3: Initialize ClientManager instance
- Step4: Add MCP servers using add_server()
- Step5: List available servers with list_servers()
- Step6: Call tools or access resources with call_tool() or read_resource()
- Step7: Manage servers dynamically as needed
EasyMCP's Core Features & Benefits
The Core Features
- Manage multiple MCP servers
- Cache management with invalidation
- Namespaced tools and resources
- Parallel server requests
- Dynamic server addition/removal
- Server lifecycle management
The Benefits
- Faster tool calls due to namespacing
- Enhanced scalability across distributed systems
- Automatic cache invalidation improves data freshness
- Lightweight asyncio support for high concurrency
EasyMCP's Main Use Cases & Applications
- Managing multiple AI model contexts in a distributed system
- Building scalable data retrieval workflows
- Integrating MCP servers in enterprise AI applications
- Real-time resource management for AI services
FAQs of EasyMCP
Developer
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