Building MCP Servers for Production
MCP standardizes how AI clients connect to tools, data, and prompts.
A production MCP server is not just a demo tool. It is an integration boundary with security and operations responsibilities.
What an MCP server exposes
| Surface | Purpose |
|---|---|
| tools | actions the model can request |
| resources | data the model can read |
| prompts | reusable prompt workflows |
Production tool design
Every tool should define:
- name
- description
- input schema
- output schema
- permission requirements
- rate limits
- error behavior
- audit logging
Security checklist
- authenticate clients
- authorize per user/workspace
- validate arguments
- redact secrets
- limit network/file access
- require approval for risky actions
- log tool calls
- version schemas
Reliability checklist
- timeouts
- retries only when safe
- structured errors
- health checks
- backward-compatible schema changes
- observability traces
- rollout and rollback plan
Knowledge check
Q1: Why is an MCP server a security boundary?
It exposes real tools and data to AI clients.
Q2: What should every production MCP tool have?
A typed schema, permission checks, validation, errors, and audit logs.