MCP Skills
Build MCP skills for AI assistants
AI assistants are only as useful as the skills they can invoke. MCP skills let you extend an assistant with custom actions, data lookups, and workflows that align with your platform and product goals.
Design skills around outcomes
Start with a clear purpose: what work should the assistant do, and what context does it need? Prefer small, composable skills with a focused intent, such as "summarize today's incident" or "create a deploy checklist." This makes the skill easier to test and reuse.
Define inputs and outputs clearly
Use structured inputs where possible: prompts, JSON payloads, or form fields. Define the expected output format before you build the skill so the assistant can reliably consume and render results. For example, return status data in a predictable object shape rather than free-form text.
Implement safety and guardrails
Skills may execute actions or surface sensitive information. Add validation, rate limiting, and role-based access controls before exposing them. Where possible, keep decision logic outside the assistant so you can audit and update policies centrally.
Test with real workflows
Run through the complete user journey: trigger the skill, verify the assistant prompt, inspect the returned result, and confirm the end user sees the right answer. Capture edge cases like missing data, unexpected formats, or downstream failures.
Integrate with your platform
Connect skills to your existing APIs, data stores, and tooling. Use event-driven patterns for asynchronous work and simple HTTP/JSON for synchronous queries. Treat the assistant as a thin orchestration layer that amplifies your platform capabilities.
Iterate based on usage
Track which skills users invoke, where the assistant fails, and when manual intervention is needed. Prioritize improvements to the skills that deliver the most value, and prune skills that are rarely used or too brittle.
Conclusion
Creating MCP skills is as much about product design as it is about integration. Focus on clear outcomes, safe execution, and measurable value, and your AI assistant becomes a practical extension of your platform.