Building an AI skill is easier than you think. In this guide, you'll go from zero to a published, functional AI skill on SkillExchange — complete with pricing, documentation, and your first potential customers. No prior MCP experience required.
What You'll Build
By the end of this guide, you'll have a text summarization skill that any MCP-compatible AI agent can discover and use. Your skill will:
- Accept any text input
- Return a concise, high-quality summary
- Include metadata (word count reduction, processing time)
- Be discoverable on the SkillExchange marketplace
- Generate revenue with per-invocation pricing
Let's get started.
Prerequisites
Before we begin, make sure you have:
- Node.js 18+ installed
- A SkillExchange account (sign up free)
- A Stripe account for receiving payments (SkillExchange uses Stripe Connect)
- Basic familiarity with TypeScript or JavaScript
Step 1: Set Up Your Project
Create a new directory for your skill and initialize it:
mkdir summarize-skill && cd summarize-skill
npm init -y
npm install @modelcontextprotocol/sdk zod
Create the basic project structure:
summarize-skill/
├── src/
│ └── index.ts
├── package.json
└── tsconfig.json
Configure TypeScript with tsconfig.json:
{
"compilerOptions": {
"target": "ES2022",
"module": "Node16",
"outDir": "./dist",
"strict": true,
"esModuleInterop": true
},
"include": ["src/**/*"]
}
Step 2: Define Your Skill's Interface
Every MCP skill starts with a clear definition of what it does. Open src/index.ts and define your skill's schema:
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";
const server = new McpServer({
name: "text-summarizer",
version: "1.0.0",
description: "Summarizes any text input into a concise, high-quality summary.",
});
server.tool(
"summarize",
"Summarize the provided text into a concise summary",
{
text: z.string().min(10).describe("The text to summarize"),
max_length: z.number().optional().describe("Maximum summary length in words"),
},
async ({ text, max_length }) => {
const startTime = Date.now();
// Your summarization logic here
const summary = generateSummary(text, max_length);
const processingTime = Date.now() - startTime;
const originalWords = text.split(/\s+/).length;
const summaryWords = summary.split(/\s+/).length;
return {
content: [
{
type: "text",
text: JSON.stringify({
summary,
metadata: {
original_word_count: originalWords,
summary_word_count: summaryWords,
reduction_percent: Math.round((1 - summaryWords / originalWords) * 100),
processing_time_ms: processingTime,
},
}),
},
],
};
}
);
function generateSummary(text: string, maxLength?: number): string {
// Implement your summarization logic
// This could call an LLM API, use a local model, or apply extractive methods
// For production, use OpenAI, Anthropic, or a local model
return "Summary of the provided text...";
}
Why This Structure Matters
The schema definition serves two critical purposes:
- Agent comprehension — AI agents read the schema to understand what your skill does and how to use it
- Input validation — Zod validates every input before it reaches your logic, preventing malformed requests
This self-describing nature is what makes MCP so powerful compared to traditional APIs.
Step 3: Add Error Handling
Production skills need robust error handling. Enhance your skill:
server.tool(
"summarize",
"Summarize the provided text into a concise summary",
{
text: z.string().min(10).describe("The text to summarize"),
max_length: z.number().min(10).max(500).optional()
.describe("Maximum summary length in words"),
},
async ({ text, max_length }) => {
try {
const startTime = Date.now();
if (text.length > 100000) {
return {
content: [{ type: "text", text: JSON.stringify({
error: "Text exceeds maximum length of 100,000 characters",
code: "INPUT_TOO_LONG"
})}],
isError: true,
};
}
const summary = await generateSummary(text, max_length);
const processingTime = Date.now() - startTime;
return {
content: [{
type: "text",
text: JSON.stringify({
summary,
metadata: {
original_word_count: text.split(/\s+/).length,
summary_word_count: summary.split(/\s+/).length,
processing_time_ms: processingTime,
},
}),
}],
};
} catch (error) {
return {
content: [{ type: "text", text: JSON.stringify({
error: "Summarization failed",
details: error instanceof Error ? error.message : "Unknown error",
})}],
isError: true,
};
}
}
);
For comprehensive error handling patterns, see our security best practices guide.
Step 4: Test Locally
Before publishing, test your skill locally:
# Build the skill
npx tsc
# Test with the MCP inspector
npx @modelcontextprotocol/inspector node dist/index.js
The inspector lets you send test inputs and verify outputs. Make sure to test:
- ✅ Normal inputs with varying text lengths
- ✅ Edge cases: very short text, very long text
- ✅ Error conditions: empty input, non-string input
- ✅ Performance: measure response time for typical inputs
Step 5: Add to the Transport Layer
For production deployment, you'll want HTTP transport (not just stdio). Create src/server.ts:
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";
import express from "express";
const app = express();
app.use(express.json());
const server = new McpServer({
name: "text-summarizer",
version: "1.0.0",
});
// ... (add your tool definition from Step 3)
app.post("/mcp", async (req, res) => {
const transport = new StreamableHTTPServerTransport({
sessionIdGenerator: undefined,
});
await server.connect(transport);
await transport.handleRequest(req, res, req.body);
});
app.listen(3000, () => {
console.log("Text Summarizer Skill running on port 3000");
});
Step 6: Deploy and Publish
Deploy Your Skill
Deploy your skill to any hosting platform (Vercel, Railway, Fly.io, AWS Lambda). The key requirement is that your skill is accessible via HTTPS.
Publish on SkillExchange
Log in to SkillExchange
Navigate to Dashboard → Skills → New Skill
Fill in the metadata:
- Name: Text Summarizer
- Description: High-quality text summarization with metadata
- Category: Text Processing
- Endpoint URL: Your deployed skill URL
- Protocol: MCP
Set your pricing — Check our pricing strategy guide for data-driven advice. For a summarization skill, $0.005–$0.01 per invocation is a good starting point.
Submit for review — SkillExchange verifies your skill works correctly before listing it.
Step 7: Optimize and Scale
Once your skill is live, focus on growth:
Monitor Performance
Track key metrics from your SkillExchange dashboard:
- Invocation count and trends
- Error rate
- Average response time
- Revenue per invocation
Our AI skill analytics guide covers the metrics that matter most.
Build Trust
Your trust score directly impacts discoverability. Maintain a high score by:
- Keeping error rates below 1%
- Responding within your advertised latency
- Handling edge cases gracefully
- Updating your skill regularly
Expand Your Portfolio
One skill is just the beginning. Build complementary skills:
- Sentiment analyzer
- Key phrase extractor
- Language detector
- Readability scorer
Related skills create a portfolio effect — agents that discover one of your skills are likely to try others.
Common Pitfalls to Avoid
Overcomplicating your first skill. Start simple, ship fast, iterate based on real usage data.
Ignoring error handling. Agents will send unexpected inputs. Your skill needs to fail gracefully, not crash. Review our best practices guide for patterns.
Underpricing. Many creators set prices too low, then struggle to raise them. Start at a sustainable price point and offer volume discounts instead.
Skipping documentation. Even though agents consume your skill's schema, human developers evaluate skills before adding them to their agents. Write clear descriptions.
Next Steps
- Read the complete MCP specification
- Join the SkillExchange Discord community
- Explore advanced monetization strategies
- Build your next skill with our zero-to-revenue playbook
You've built your first AI skill. Now publish it and start earning. The marketplace is waiting.
Frequently Asked Questions
How long does it take to build an MCP skill? A basic skill can be built and published in 30 minutes to 2 hours. More complex skills with multiple tools and integrations may take a few days.
Do I need to know AI/ML to build a skill? Not necessarily. Many skills wrap existing APIs or perform straightforward data processing. Skills that use AI internally can call external LLM APIs.
How much can I earn from a skill? Earnings vary widely. Simple skills might generate $50–$200/month. Specialized, high-demand skills can earn $1,000–$10,000+/month. See our pricing playbook for strategies.
What programming languages are supported? MCP has official SDKs for TypeScript/JavaScript and Python. Community SDKs exist for Rust, Go, and other languages. Any language that can implement the MCP protocol specification works.
Can I update a published skill? Yes. You can update your skill's code, pricing, and metadata at any time through the SkillExchange dashboard. Changes are reflected immediately for new invocations.