The Model Control Protocol Server (MCP) is a CLI tool that sets up and deploys an MCP server to Cloudflare Workers, enabling developers to quickly create tools for their Cursor Agent using TypeScript functions with JSDoc comments. It simplifies the process of deploying and managing servers by automating tasks such as cloning templates, installing dependencies, initializing Git repositories, and deploying to Cloudflare.
The MCP server eliminates the need to run local servers or manage node processes for simple API calls in Cursor. It offers a streamlined developer experience (DX), fast deployments, and seamless integration with Cloudflare Workers. Developers can focus on writing functions while leveraging JSDoc comments for descriptions and parameters, making it easy to build and deploy tools efficiently.
Developers and AI enthusiasts who want to create and deploy custom tools for their Cursor Agent without dealing with complex server setups or maintenance should use MCP. It's particularly beneficial for those familiar with TypeScript and Cloudflare Workers.
An MCP server can be deployed to Cloudflare Workers, which provides a scalable and efficient environment for running serverless functions. This ensures blazing-fast performance and minimal operational overhead.
You should use MCP when you need to quickly prototype, develop, and deploy tools for your Cursor Agent without worrying about infrastructure management. It’s ideal for scenarios where rapid iteration and deployment are required.
To get started, ensure you have the Wrangler CLI installed and logged into your Cloudflare account. Then, run `bun create mcp` to scaffold and deploy a new MCP server. You can also specify a server name like `bun create mcp <server-name>`.
You need the Wrangler CLI installed and logged in with your Cloudflare account, as well as the Claude Desktop App installed to integrate your MCP tools with Cursor.
Add functions to the `MyWorker` class in `src/index.ts`. Use JSDoc comments to define the tool's description, parameters, and return values. After making changes, redeploy the worker using `bun run deploy` and reload your Cursor window.
Yes! Contributions and feedback are welcome. You can submit a Pull Request or open an issue on the project repository.
The project was inspired by `workers-mcp` created by @geelen. It aims to simplify the development and deployment of MCP tools by leveraging Cloudflare Workers for a seamless experience.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.