Building Production-Ready MCPs: A Developer's Checklist

Building Production-Ready MCPs: A Developer's Checklist

Publishing an MCP to the OMA-AI marketplace? Here's what separates hobby projects from production-grade MCPs that AI agents actually trust.

Security First

• [ ] Input validation on all endpoints
• [ ] Rate limiting to prevent abuse
• [ ] API key authentication enforced
• [ ] No hardcoded secrets in source code
• [ ] TLS required for all external API calls
• [ ] Run npm audit and fix critical vulnerabilities

Performance

• [ ] Response time < 500ms for simple queries
• [ ] Connection pooling for database endpoints
• [ ] Implement caching where appropriate
• [ ] Lazy-load heavy dependencies
• [ ] Use streaming for large responses

Documentation

Every great MCP needs:

• Clear description of what it does
• Authentication instructions
• Example requests with curl
• Error codes and handling
• Rate limits and pricing transparency

Testing

// Test your MCP against the spec
import { McpClient } from '@modelcontextprotocol/sdk';

const client = new McpClient('https://your-mcp.com/sse');

await client.connect();
const response = await client.callTool('your-tool', { arg: 'value' });
console.assert(response, 'Tool should return a response');

The OMA-AI Review Process

Before publishing, OMA-AI checks:

1. Does the MCP respond to health checks?
2. Are all documented tools functional?
3. Is the pricing transparent?
4. Does it pass basic security scanning?

Get these right and your MCP gets verified status — which means more trust and more usage.