xmcp Demo Application
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@xmcp Demo Applicationgreet me with the name Alex"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
xmcp Application
This project was created with create-xmcp-app.
Getting Started
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm devThis will start the MCP server with the selected transport method.
Related MCP server: Discord MCP Server
Project Structure
This project uses the structured approach where tools, prompts, and resources are automatically discovered from their respective directories:
src/tools- Tool definitionssrc/prompts- Prompt templatessrc/resources- Resource handlers
Tools
Each tool is defined in its own file with the following structure:
import { z } from "zod";
import { type InferSchema, type ToolMetadata } from "xmcp";
export const schema = {
name: z.string().describe("The name of the user to greet"),
};
export const metadata: ToolMetadata = {
name: "greet",
description: "Greet the user",
annotations: {
title: "Greet the user",
readOnlyHint: true,
destructiveHint: false,
idempotentHint: true,
},
};
export default function greet({ name }: InferSchema<typeof schema>) {
return `Hello, ${name}!`;
}Prompts
Prompts are template definitions for AI interactions:
import { z } from "zod";
import { type InferSchema, type PromptMetadata } from "xmcp";
export const schema = {
code: z.string().describe("The code to review"),
};
export const metadata: PromptMetadata = {
name: "review-code",
title: "Review Code",
description: "Review code for best practices and potential issues",
role: "user",
};
export default function reviewCode({ code }: InferSchema<typeof schema>) {
return `Please review this code: ${code}`;
}Resources
Resources provide data or content with URI-based access:
import { z } from "zod";
import { type ResourceMetadata, type InferSchema } from "xmcp";
export const schema = {
userId: z.string().describe("The ID of the user"),
};
export const metadata: ResourceMetadata = {
name: "user-profile",
title: "User Profile",
description: "User profile information",
};
export default function handler({ userId }: InferSchema<typeof schema>) {
return `Profile data for user ${userId}`;
}Adding New Components
Adding New Tools
To add a new tool:
Create a new
.tsfile in thesrc/toolsdirectoryExport a
schemaobject defining the tool parameters using ZodExport a
metadataobject with tool informationExport a default function that implements the tool logic
Adding New Prompts
To add a new prompt:
Create a new
.tsfile in thesrc/promptsdirectoryExport a
schemaobject defining the prompt parameters using ZodExport a
metadataobject with prompt information and roleExport a default function that returns the prompt text
Adding New Resources
To add a new resource:
Create a new
.tsfile in thesrc/resourcesdirectoryUse folder structure to define the URI (e.g.,
(users)/[userId]/profile.ts→users://{userId}/profile)Export a
schemaobject for dynamic parameters (optional for static resources)Export a
metadataobject with resource informationExport a default function that returns the resource content
Building for Production
To build your project for production:
npm run build
# or
yarn build
# or
pnpm buildThis will compile your TypeScript code and output it to the dist directory.
Running the Server
You can run the server for the transport built with:
HTTP:
node dist/http.jsSTDIO:
node dist/stdio.js
Given the selected transport method, you will have a custom start script added to the package.json file.
For HTTP:
npm run start-http
# or
yarn start-http
# or
pnpm start-httpFor STDIO:
npm run start-stdio
# or
yarn start-stdio
# or
pnpm start-stdioLearn More
demo-xmcp
This server cannot be deployed
Maintenance
Related MCP Connectors
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
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