Simplify MCP server creation in TypeScript with EasyMCP's easy-to-use, Express-like syntax for building resources and tools
EasyMCP is the simplest way to create Model Context Protocol (MCP) servers in TypeScript, designed for developers looking to integrate AI applications with specific data sources and tools through a standardized protocol. This server hides complex plumbing behind simple functions, making it easy to define resources, templates, tools, and prompts without worrying about low-level details.
EasyMCP provides a minimalistic API for building MCP servers by mimicking the syntax of popular server frameworks like Express. It allows users to focus on defining the necessary MCP capabilities rather than handling complex configurations or protocols. Key features include:
These features enable seamless integration with AI applications such as Claude Desktop, Continue, and Cursor. Users can specify the exact resources and functionality their AI application needs without delving into protocol-specific code.
MCP is a universal adapter for integrating AI applications with various data sources and tools using a standardized protocol. This protocol ensures that AI applications can communicate seamlessly with servers and utilize the provided functionalities without needing to understand low-level details.
graph TD
A[AI Application] -->|MCP Client| B[MCP Server]
B --> C[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
This diagram illustrates the flow from an AI application to a MCP server and finally to a data source or tool. The MCP client acts as an intermediary, handling communication between different components.
| MCP Client | Resources | Tools | Prompts | Status |
|---|---|---|---|---|
| Claude Desktop | ✅ | ✅ | ✅ | Full Support |
| Continue | ✅ | ✅ | ✅ | Full Support |
| Cursor | ❌ | ✅ | ❌ | Tools Only |
This matrix highlights the compatibility of EasyMCP with various AI applications, indicating full support for resources and prompts while tools are only partially supported.
To get started with EasyMCP, follow these steps:
pnpm install easy-mcp
bun add easy-mcp
Imagine an AI application that needs to access files on a user's desktop. Using EasyMCP, you can define a resource for the desktop directory:
mcp.resource({
uri: "dir://desktop",
name: "Desktop Directory",
description: "Lists files on the desktop",
fn: async () => {
return "file://desktop/file1.txt\nfile://desktop/file2.txt";
},
});
Another use case might involve creating a tool that greets users based on their name:
mcp.tool({
name: "greet",
inputs: [
{
name: "name",
type: "string",
required: true,
},
],
fn: async ({ name }) => {
return `Hello, ${name}!`;
},
});
These use cases demonstrate how EasyMCP simplifies the process of integrating different functionalities into an AI application.
EasyMCP is designed to be compatible with popular AI clients such as Claude Desktop, Continue, and Cursor. Developers can easily extend the server capabilities by defining additional resources, templates, tools, or prompts. This makes it highly adaptable for various use cases in AI workflows.
EasyMCP has been tested against different MCP clients to ensure broad compatibility:
| Client | Resources | Tools | Prompts |
|---|---|---|---|
| Claude Desktop | ✅ | ✅ | ✅ |
| Continue | ✅ | ✅ | ✅ |
| Cursor | ❌ | ✅ | ❌ |
This matrix provides an overview of the current compatibility status, ensuring that EasyMCP fits well within the MCP ecosystem.
For advanced users,EasyMCP supports custom configurations through environment variables. Here’s a sample configuration:
{
"mcpServers": {
"[server-name]": {
"command": "node",
"args": ["-r", "path/to/your/server.js"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
To enhance security, users can set environment variables or define custom command and arguments for running the server. This ensures that sensitive information like API keys are properly managed.
fn callback to return dynamic content based on user inputs or other conditions.These FAQs address common questions related to integrating EasyMCP into various AI workflows and managing client compatibility.
Contributions are welcome! Developers can contribute to the project by submitting pull requests. If you need assistance or have ideas, feel free to reach out to the community through the repository issues or mailing list.
EasyMCP is part of a larger ecosystem aimed at standardizing AI application integration across different tools and data sources. For more information on other tools and resources within this ecosystem, visit the EasyMCP GitHub page [link].
By following these guidelines and exploring the comprehensive documentation, developers can effectively integrate EasyMCP into their AI workflows, enhancing functionality and interoperability with various MCP clients.
This content provides a detailed guide to using EasyMCP, adhering strictly to the README information while optimizing for technical accuracy, English language, and specific keywords.
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