Model Context Protocol (MCP): The Future of AI Tools

The way we build AI agents has changed with the adoption of Model Context Protocol (MCP), introduced by Anthropic.

What is MCP?

Historically, giving an agent such as Claude or a custom LangGraph bot access to a database or a service like GitHub required specific API connectors embedded in the AI application.

MCP standardizes that communication. A local or remote server exposes tools, resources, and prompts through a common protocol. An AI client connects to the MCP server and discovers its capabilities.

Benefits for developer experience (DX)

1. Decoupling: The tools server does not need to know which LLM you use. Write the server once, for example in TypeScript or Python, and compatible clients can consume it.
2. Local control: Run MCP servers locally and give your editor granular permissions to read files or execute commands.
3. Production architecture: Deploy MCP servers in separate containers so that services can provide capabilities to the central RAG workflow.

In recent projects, I have moved tools such as PostgreSQL calls and WhatsApp queue management to MCP. This has helped organize automation workflows.