使用说明
MCP Deployment Patterns
This repository documents production-ready deployment patterns and operational best practices for MCP servers used by agentic AI systems.
Why MCP server deployments are riskier than traditional APIs
MCP servers often expose tools that can read and write data, trigger workflows, and execute actions with real-world impact. That means the blast radius of mistakes is larger, and the failure modes are less predictable. Agent behavior can also amplify small configuration errors into high-volume or high-impact actions.
Why agentic AI changes deployment assumptions
Agentic systems make decisions, chain actions, and call tools in loops. This changes how you must think about rate limits, isolation, retries, and observability. Deployments must account for probabilistic behavior, multi-step tool use, and the need to safely stop or constrain agents in real time.
What this repo covers
- Deployment patterns that reduce blast radius and risk
- Environment separation and configuration isolation
- Security, auditability, and incident readiness
- Scaling and concurrency for tool-heavy workloads
- Safe rollouts, rollbacks, and kill switches
- Common anti-patterns to avoid
- Minimal, illustrative examples
What this repo does not cover
- Cloud-specific or vendor-specific configurations
- Full Terraform, Helm charts, or production templates
- One-size-fits-all reference architectures
- Basic MCP concepts or protocol introductions
How this repo fits into the MCP ecosystem
This repository complements MCP protocol documentation and SDKs by focusing on operational safety and production deployment patterns. It is designed to be used alongside official MCP specs and tool implementations, filling the gap between protocol design and real-world operations.
