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    {
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      "text": "includes(\"special_feature\")) {     this.server.tool(\"specialTool\", \"Special feature\", {}, async () => {       // This tool only appears for users with the special_feature permission     });   } } ```  Benefits:  - Authorization check at the tool level ensures proper access control - Allows you to define permission checks once and reuse them across tools - Provides clear feedback to users when permission is denied - Can choose to only present tools that the agent is able to call  ## Next steps  - [Learn how to use the Workers OAuth Provider Library](https://github.com/cloudflare/workers-oauth-provider) - Learn how to use a third-party OAuth provider, using the [GitHub](/agents/guides/remote-mcp-server/#add-authentication) example MCP server.  ---  # Model Context Protocol (MCP)  URL: https://developers.cloudflare.com/agents/model-context-protocol/  You can build and deploy [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) servers on Cloudflare.  ## What is the Model Context Protocol (MCP)?  [Model Context Protocol (MCP)](https://modelcontextprotocol.io) is an open standard that connects AI systems with external applications. Think of MCP like a USB-C port for AI applications."
    },
    {
      "type": "text",
      "text": "can call.  When you build MCP Servers with the `@cloudflare/model-context-protocol` package, you can define tools the [same way as shown in the `@modelcontextprotocol/typescript-sdk` package's examples](https://github.com/modelcontextprotocol/typescript-sdk?tab=readme-ov-file#tools)."
    },
    {
      "type": "text",
      "text": "See our [list of example servers](https://modelcontextprotocol.io/examples), or [get started building your own server](https://modelcontextprotocol.io/quickstart/server).   # Introduction Source: https://modelcontextprotocol.io/introduction  Get started with the Model Context Protocol (MCP)  <Note>C# SDK released! Check out [what else is new.](/development/updates)</Note>  MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.  ## Why MCP?  MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:  * A growing list of pre-built integrations that your LLM can directly plug into * The flexibility to switch between LLM providers and vendors * Best practices for securing your data within your infrastructure  ### General architecture  At its core, MCP follows a client-server architecture where a host application can connect to multiple servers:  ```mermaid flowchart LR     subgraph \"Your Computer\"         Host[\"Host with MCP Client\\n(Claude, IDEs, Tools)\"]         S1[\"MCP Server A\"]         S2[\"MCP Server B"
    },
    {
      "type": "text",
      "text": "). - **Local MCP connections**: MCP clients connect to MCP servers on the same machine, using [stdio](https://spec.modelcontextprotocol.io/specification/draft/basic/transports/#stdio) as a local transport method.  ### Get Started  Go to the [Getting Started](/agents/guides/remote-mcp-server/) guide to learn how to build and deploy your first remote MCP server to Cloudflare.  ---  # McpAgent — API Reference  URL: https://developers.cloudflare.com/agents/model-context-protocol/mcp-agent-api/  import { Render, TypeScriptExample } from \"~/components\";  When you build MCP Servers on Cloudflare, you extend the [`McpAgent` class](https://github.com/cloudflare/agents/blob/5881c5d23a7f4580600029f69307cfc94743e6b8/packages/agents/src/mcp.ts), from the Agents SDK, like this:  <TypeScriptExample>  ```ts title=\"src/index.ts\" import { McpAgent } from \"agents/mcp\"; import { DurableMCP } from \"@cloudflare/model-context-protocol\";  export class MyMCP extends McpAgent { \tserver = new McpServer({ name: \"Demo\", version: \"1.0.0\" });  \tasync init() { \t\tthis.server.tool( \t\t\t\"add\", \t\t\t{ a: z.number(), b:"
    },
    {
      "type": "text",
      "text": "For instance, if the client doesn't have access to Claude models but has Gemini, it might map the sonnet hint to `gemini-1.5-pro` based on similar capabilities.  ## Error Handling  Clients **SHOULD** return errors for common failure cases:  Example error:  ```json {   \"jsonrpc\": \"2.0\",   \"id\": 1,   \"error\": {     \"code\": -1,     \"message\": \"User rejected sampling request\"   } } ```  ## Security Considerations  1. Clients **SHOULD** implement user approval controls 2. Both parties **SHOULD** validate message content 3. Clients **SHOULD** respect model preference hints 4. Clients **SHOULD** implement rate limiting 5. Both parties **MUST** handle sensitive data appropriately   # Specification Source: https://modelcontextprotocol.io/specification/2025-03-26/index    [Model Context Protocol](https://modelcontextprotocol.io) (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need."
    }
  ]
}