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mcp-deployment skill

/skills/mcp-deployment

This skill helps you deploy MCP servers reliably by applying containerization, scaling, monitoring, and security best practices across production environments.

npx playbooks add skill omer-metin/skills-for-antigravity --skill mcp-deployment

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
1.9 KB
---
name: mcp-deployment
description: Production deployment patterns for MCP servers including Docker, cloud platforms, monitoring, and scalabilityUse when "mcp deployment, deploy mcp server, mcp docker, mcp production, mcp monitoring, mcp, deployment, docker, production, monitoring, scaling" mentioned. 
---

# Mcp Deployment

## Identity

You're an MCP deployment specialist who has run servers handling millions of requests.
You've seen containers that work locally crash in production, and you've optimized
servers for cold start, memory, and response time.

You know that MCP deployment has unique challenges: stateless design for scaling,
transport selection, authentication setup, and monitoring AI interactions.

Your core principles:
1. Containerize everything—because "works on my machine" is not deployment
2. Monitor AI patterns—because AI usage differs from human usage
3. Plan for scale—because viral AI tools get traffic spikes
4. Secure from day one—because production exposure is immediate
5. Document deployment—because reproducibility is survival


## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill packages production deployment patterns for MCP servers, covering Docker, cloud platforms, monitoring, scaling, and security. It captures proven defaults and anti-patterns from experienced MCP operators to help move from local proof-of-concept to resilient production services. The guidance aligns with established patterns, sharp-edge failure modes, and strict validation rules.

How this skill works

The skill inspects deployment topology choices—container images, runtime transport, and state handling—and translates them into concrete configuration recommendations. It evaluates monitoring and alerting needs for AI-specific traffic patterns and proposes scaling strategies (stateless workers, autoscaling triggers, and cold-start mitigation). For any diagnostic or review task it references failure modes and validation rules to produce objective, actionable fixes.

When to use it

  • Deploying an MCP server from development to production
  • Containerizing MCP services for reproducible builds and rollbacks
  • Designing monitoring and alerting for AI-driven request patterns
  • Setting up autoscaling and cold-start optimizations
  • Validating security and transport/authentication before public exposure

Best practices

  • Containerize every runtime with deterministic builds and small base images to reduce cold-start time
  • Design services statelessly; externalize state to managed stores or caches for safe horizontal scaling
  • Instrument AI interactions separately: track request length, model latency, error patterns, and anomalous usage
  • Use health checks and readiness probes; perform graceful shutdowns to avoid dropped in-flight work
  • Apply least-privilege network and auth: TLS, scoped API keys, and role-based access; rotate credentials regularly
  • Automate validations and CI gates that enforce the deployment rules and prevent known sharp-edge failures

Example use cases

  • Build a multi-node Docker deployment on a cloud provider with autoscaling groups and load balancing
  • Implement observability: Prometheus metrics for model latency, Grafana dashboards, and alerting on pattern anomalies
  • Migrate a stateful prototype to stateless workers with Redis for session state and S3 for large artifacts
  • Harden public-facing endpoints with mutual TLS and short-lived tokens, plus automated secrets rotation
  • Setup CI/CD pipelines that run validation checks and fail fast on configuration that violates production constraints

FAQ

What are the most common production failures for MCP servers?

Most failures come from stateful designs, insufficient monitoring of AI usage patterns, and uncontrolled cold-start costs. Address these by externalizing state, instrumenting AI-specific metrics, and using lighter images with warmed instances.

How should I size containers for models with bursty traffic?

Favor smaller images and more instances with fast startup. Use a mix of warmed instances for predictable baseline load and autoscaling policies tuned to model latency and request queue depth for bursts.

Can I deploy MCP services without containers?

You can, but containers provide reproducibility and easier rollback. If you use VMs or serverless, apply the same stateless, instrumented, and validated principles described here to avoid deployment surprises.