MCP Workers AI

What is MCP Workers AI?

MCP Workers AI is an SDK for integrating Model Context Protocol (MCP) server tools with Cloudflare Workers. It allows developers to load multiple MCP servers (e.g., GitLab, Slack) and call their tools from Worker scripts, feeding the results into Cloudflare Workers AI model inference requests.

How to use MCP Workers AI?

Install the package via npm or yarn, then import loadTools and callTool. Configure your Worker with an AI binding and necessary environment variables (e.g., tokens for external services). In a fetch handler, load the MCP tools, send a user prompt to a Workers AI model, and handle any tool calls the model returns.

Key features of MCP Workers AI

• Load multiple MCP server tools in a single Worker.
• Call tools based on AI model‑selected tool calls.
• Integrates seamlessly with Cloudflare Workers AI.
• Supports tool result handling for multi‑turn conversations.
• Works with standard MCP servers (e.g., GitLab, Slack).

Use cases of MCP Workers AI

• Automate GitLab repository operations via natural language prompts.
• Let an AI assistant interact with Slack channels and files.
• Build Workers that combine external service actions with AI reasoning.
• Prototype agent‑like workflows on Cloudflare’s edge platform.

FAQ from MCP Workers AI

What is MCP?

MCP stands for Model Context Protocol, a protocol for providing tools to AI models. MCP Workers AI enables using MCP servers with Cloudflare Workers.

What dependencies are required?

You need a Cloudflare Workers project with an AI binding ([ai] binding = "AI") and environment variables for the external services you connect to (e.g., GITLAB_PERSONAL_ACCESS_TOKEN).

Where does the tool data live?

Tool data is fetched from the external services at the time of invocation. The Worker environment holds credentials; no data is stored permanently by the SDK.

Which MCP servers are supported?

Any standard MCP server module compatible with the @modelcontextprotocol/sdk can be imported. Examples include @modelcontextprotocol/server-gitlab and @modelcontextprotocol/server-slack.

How are tools invoked?

The SDK exports callTool, which takes an object with name and arguments (typically provided by the AI model) and returns the tool’s response. That response can then be fed back into a subsequent AI inference call.