MCP

The Model Context Protocol (MCP) allows large language models (LLMs) to call external tools while answering user prompts. In practice, most people use chat-based tools like Claude or Copilot rather than interacting directly with model APIs, and these tools make it easy to configure MCP servers without writing code. That said, if you want to connect your own data sources or custom functions as MCP tools, or connect your code to MCP tools, you’ll have to roll up your sleeves and do some hands-on work. ...

As we integrate services and data APIs into agentic AI solutions, interest is growing in how the Model Context Protocol (MCP) can standardize the way tools expose their capabilities to agents. With that in mind, I’ve assembled—yes, with the help of AI—a survey of key topics and resources related to MCP. MCP is an open standard (launched by Anthropic in Nov 2024) for exposing data sources, tools, and “resources” to AI agents via a uniform interface. It is designed to replace the ad-hoc “one-off connector per tool/agent” pattern, simplifying how LLM-based agents integrate with live systems. [1] ...

The first time I fired up an MCP (Model Context Protocol) server plugin, “Agent,” I was excited to see it registered in Claude Desktop but immediately annoyed by the errors that popped up. I didn’t expect a smooth experience in my encounter with the future of Agentic AI, but I found many configuration tweaks, clunky debugging tools, and broken dependencies along the way. It was a stark reminder that we’re in the early days, and there’s a lot of ground to cover before Agents become seamless collaborators. ...