I previously covered MCP on my YouTube channel:
https://www.youtube.com/watch?v=cA6tZiR7qcs
On May 22, Anthropic releases new capabilities for building agents on the Anthropic API including MCP connector
We’re releasing four new capabilities on the Anthropic API that enable developers to build more powerful AI agents… MCP connector
For example, a project management AI agent can use the MCP connector with Asana to reference tasks and assign work, upload relevant reports via the Files API, analyze progress and risks with the code execution tool, and maintain full context throughout—all while keeping costs down through extended prompt caching
Let’s dive deeper into their MCP connector
What does it do?
It enables developers to connect Claude to any remote MCP server without writing ‘client code’
So how does it work?
Previously, connecting to MCP servers required building your own client harness to handle MCP connections. Now, the Anthropic API handles all connection management, tool discovery, and error handling automatically. Simply add a remote MCP server URL to your API request and you can immediately access powerful third-party tools, dramatically reducing the complexity of building tool-enabled agents.
So the MCP connector means Anthropic’s API now automatically ‘retrieves available tools’ and ‘reasons about what tool to call.’ I can’t wait for my Claude agent to spend 30 seconds reasoning about whether to use the ‘Asana: Create Task’ tool or the ‘Asana: Also Create Task But Slightly Different’ tool.
Remember the dark ages? When developers had to think about how their Claude agent would talk to an MCP server? Perish the thought! Now, it’s all handled. Your agent can now seamlessly… delegate. Truly the peak of AI autonomy.
Jokes aside, developers can connect Claude, including Claude Code, to any MCP server!
What are some examples of MCP servers?
- Access External Tools: AI applications connect to MCP servers to use their specialized tools and services via a common interface.
- Enable Interoperability: Allow diverse AI applications to communicate and work with various services without custom integrations.
- Perform Specific Tasks: Use MCP servers for functions like web searching (for research) or accessing logs/metrics (for context).
- Secure Code Execution: Connect to an MCP server to run code (e.g., Python) in a sandboxed, secure environment.
- Extend AI Capabilities: Programmatic agents can use MCP servers to broaden their range of available actions and functionalities.