Title: The USB C Of AI: Make Your Own MCP

Anthropic has open-sourced the Model Context Protocol (MCP), a plug-in standard that lets any AI agent talk to databases, monitoring tools, code repositories and more through a single, uniform interface. Developers can now replace a handful of bespoke connectors with one MCP server and let every compatible client access the same data instantly.

Why AI-app developers need a common “USB-C”

Building an AI-powered workflow today feels like stitching together custom code. Want Claude, a Python script, or a third-party editor to read from PostgreSQL, query a Datadog dashboard, or open a GitHub issue? You write a new integration layer each time. The effort balloons as the stack grows, and the glue code becomes hard to maintain, test, and secure.

MCP stops that cycle. It defines a single protocol for exposing resources (read-only data such as logs or API specs), tools (actions an agent can invoke, like running SQL or creating tickets), and prompts (templates that steer how the AI should use the data). Any compliant client treats disparate services as if they were plugged into the same port.

How the protocol is structured

  • MCP Hosts – applications that embed an AI, for example Claude Desktop or a code-assistant editor.
  • MCP Clients – thin layers inside a host that handle the handshake and route requests.
  • MCP Servers – lightweight programs that expose a specific data source or service, such as a SQLite file, a Slack workspace, or a monitoring endpoint.

When an AI connects, the client discovers the server’s catalog of resources, tools and prompts. The agent then issues calls that look identical whether the underlying service is a relational database or a ticketing system. The server translates those calls into the appropriate API or query language and returns results in a format the agent can consume.

A concrete workflow

Imagine a developer who hooks a PostgreSQL server and a Datadog monitoring endpoint to an AI assistant via MCP. The developer asks the assistant to locate recent error spikes, pull the corresponding user records from the database, and suggest a code fix. The AI:

  1. Queries Datadog through the MCP-exposed monitoring tool and pulls the relevant logs.
  2. Executes a SQL query on the PostgreSQL server using the MCP-provided database tool.
  3. Generates a patch from a prompt that describes the typical structure of a fix for the observed error pattern.

All three steps happen through the same protocol, without the developer writing separate adapters for each service.

Building an MCP server

Anthropic’s reference implementation shows that a server can be written in familiar languages such as TypeScript or Python. The process boils down to:

  1. Declaring the resources the server will expose (e.g., a set of tables, a collection of log files).
  2. Defining the tools the AI may invoke (SQL execution, HTTP POST, file write).
  3. Providing any prompt templates that help the AI format its requests.

Once the server runs, any MCP-compatible client discovers it automatically, making the new data source instantly usable across the organization’s AI tools.

Takeaway

The Model Context Protocol offers a single “USB-C” for AI agents, turning a tangled web of custom connectors into a plug-and-play ecosystem. Teams tired of writing glue code can test MCP today and see a practical shortcut in action.