Оффлайн
- Регистрация
- 1 Мар 2015
- Сообщения
- 1,481
- Баллы
- 155
Introduction: What Is Docker MCP and Why It Matters
The rise of agent-based AI applications, powered by ChatGPT, Claude, and custom LLMs, has created a demand for modular, secure, and standardized integrations with real-world tools. Docker’s Model Context Protocol (MCP), along with its Catalog and Toolkit, addresses this need.
Docker is positioning itself not just as a container platform but as the infrastructure backbone for intelligent agents. In this post, we’ll explore the MCP architecture, Catalog, and Toolkit, and demonstrate how to build your own MCP server.
Section 1: Understanding MCP: The Model Context Protocol
What it is:
Why it matters:
Visual overview:
How an AI client communicates with containerized services via MCP
Section 2: MCP Catalog: Prebuilt, Secure MCP Servers
What it includes:
A growing library of 100+ Docker-verified MCP servers, including:
Key features:
Each MCP server runs inside a container and includes:
Why developers care:
Visual overview:
MCP Catalog integration with Docker Desktop
Section 3: MCP Toolkit: Build Your Own Secure MCP Server
Toolkit CLI Features:
Security features:
Demo walkthrough:
npm install -g @docker/mcp-toolkit
mcp init my-weather-api
cd my-weather-api
mcp run
Visual walkthrough:
MCP Toolkit Workflow: From CLI to Container
Section 4: Connecting MCP Servers to AI Clients
Supported clients:
How it works:
Use case example:
Claude uses a Docker MCP server to call a Stripe payment processing container during an e-commerce interaction.
Visual flow:
Shows how Claude securely calls a Stripe service via Docker MCP.
Section 5: Best Practices for MCP Server Developers
Security:
Performance:
Testing tips:
Predictions:
Opportunities for devs:
Closing Thoughts
Docker’s MCP Catalog and Toolkit are still in beta, but the path forward is clear: AI apps need real-world tool access, and Docker is building a secure, open ecosystem to power it.
Whether you’re building agent frameworks or just experimenting with tool-using LLMs, now’s the perfect time to get involved.
Got ideas for MCP servers you want to see? Or thinking about contributing your own? I’d love to hear from you! ?
The rise of agent-based AI applications, powered by ChatGPT, Claude, and custom LLMs, has created a demand for modular, secure, and standardized integrations with real-world tools. Docker’s Model Context Protocol (MCP), along with its Catalog and Toolkit, addresses this need.
Docker is positioning itself not just as a container platform but as the infrastructure backbone for intelligent agents. In this post, we’ll explore the MCP architecture, Catalog, and Toolkit, and demonstrate how to build your own MCP server.
Section 1: Understanding MCP: The Model Context Protocol
What it is:
- MCP is an open protocol that allows AI clients (like agents) to call real-world services securely and predictably.
- It's designed for tool interoperability, secure credential management (handling API keys and tokens), and container-based execution.
Why it matters:
- Without standards like MCP, agents rely on brittle APIs or unsafe plugins.
- Docker provides a secure, isolated runtime to host these services in containers.
Visual overview:
How an AI client communicates with containerized services via MCP
Section 2: MCP Catalog: Prebuilt, Secure MCP Servers
What it includes:
A growing library of 100+ Docker-verified MCP servers, including:
- Stripe
- LangChain
- Elastic
- Pinecone
- Hugging Face
Key features:
Each MCP server runs inside a container and includes:
- OpenAPI spec
- Secure default config
- Docker Desktop integration
Why developers care:
- Plug-and-play tools for AI agents.
- Consistent dev experience across services.
Visual overview:
MCP Catalog integration with Docker Desktop
Section 3: MCP Toolkit: Build Your Own Secure MCP Server
Toolkit CLI Features:
- mcp init → Scaffolds new MCP server
- mcp run → Runs local dev version
- mcp deploy → Deploy to Docker Desktop
Security features:
- Container isolation
- OAuth support for credentials
- Optional rate limiting and tracing
Demo walkthrough:
npm install -g @docker/mcp-toolkit
mcp init my-weather-api
cd my-weather-api
mcp run
Visual walkthrough:
MCP Toolkit Workflow: From CLI to Container
Section 4: Connecting MCP Servers to AI Clients
Supported clients:
- Claude (Anthropic)
- GPT Agents (OpenAI)
- Docker AI (beta)
- VS Code Extensions
How it works:
- Agents call /invoke endpoint defined in MCP spec.
- Secure token exchange handles identity.
- Response returned to model for reasoning/action.
Use case example:
Claude uses a Docker MCP server to call a Stripe payment processing container during an e-commerce interaction.
Visual flow:
Shows how Claude securely calls a Stripe service via Docker MCP.
Section 5: Best Practices for MCP Server Developers
Security:
- Never use root containers
- Use docker scan and trivy for image vulnerability scanning
- Store secrets with Docker's secret manager (or Vault)
Performance:
- Keep containers lightweight (use Alpine or Distroless)
- Use streaming responses for LLM interaction
Testing tips:
- Use Postman + curl to test /invoke endpoint
- Lint OpenAPI specs with swagger-cli
Predictions:
- Docker AI Dashboard integration
- MCP orchestration (multiple services per agent)
- AI-native DevOps (agents building infra with MCP servers)
Opportunities for devs:
- Contribute to open MCP servers
- Submit to Docker Catalog
- Build agent tools for internal or public use
Closing Thoughts
Docker’s MCP Catalog and Toolkit are still in beta, but the path forward is clear: AI apps need real-world tool access, and Docker is building a secure, open ecosystem to power it.
Whether you’re building agent frameworks or just experimenting with tool-using LLMs, now’s the perfect time to get involved.
Got ideas for MCP servers you want to see? Or thinking about contributing your own? I’d love to hear from you! ?