Anthropic is trying SO hard to fix MCP...

In this monologue podcast, Theo dives deep into Anthropic's new "Advanced tool use" on the Claude developer platform, critically analyzing its approach to Model Context Protocol (MCP). He discusses the inefficiencies of MCP, where every tool definition is included in each prompt, making the process expensive, slow, and cumbersome. Theo explores Anthropic's proposed solutions, including the ToolSearchTool and ProgrammaticToolCallingTool, which aim to dynamically discover, learn, and execute tools. He also touches on the importance of code execution for tool orchestration, the limitations of JSON schemas, and potential prompt injection vulnerabilities. The podcast includes a brief sponsor mention for Greptile, an AI code review tool, and concludes with Theo's overall skepticism about the current state of AI tool usage and the increasing complexity of building AI-driven systems.

Outlines

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Theo - t3․gg

Introduction to Model Context Protocol (MCP) and Anthropic's Advanced Tool Use

The Inefficiencies of MCP and Anthropic's Proposed Solutions

Code Execution as a Solution to MCP's Limitations

Anthropic's New Features: ToolSearchTool, ProgrammaticToolCallingTool, and Tool Use Examples

Deep Dive into ToolSearchTool and Context Management

Caching Strategies and Potential Prompt Injection Vulnerabilities

When to Use ToolSearchTool and Introduction to Programmatic Tool Calling

Programmatic Tool Calling: Addressing Inference Overhead and Improving Accuracy

Python vs. TypeScript for Programmatic Tool Calling and Introduction to Tool Use Examples

Tool Use Examples and Best Practices for Combining Features

The Layers of Abstraction and the Need for Concise Tool Definitions

The Need for Better Protocols and a Potential Acquisition of Zedd

Anthropic is trying SO hard to fix MCP...

Theo - t3․gg

00:00Introduction to Model Context Protocol (MCP) and Anthropic's Advanced Tool Use

Introduction to Model Context Protocol (MCP) and Anthropic's Advanced Tool Use

04:30The Inefficiencies of MCP and Anthropic's Proposed Solutions

The Inefficiencies of MCP and Anthropic's Proposed Solutions

08:10Code Execution as a Solution to MCP's Limitations

Code Execution as a Solution to MCP's Limitations

11:41Anthropic's New Features: ToolSearchTool, ProgrammaticToolCallingTool, and Tool Use Examples

Anthropic's New Features: ToolSearchTool, ProgrammaticToolCallingTool, and Tool Use Examples

15:20Deep Dive into ToolSearchTool and Context Management

Deep Dive into ToolSearchTool and Context Management

19:09Caching Strategies and Potential Prompt Injection Vulnerabilities

Caching Strategies and Potential Prompt Injection Vulnerabilities

22:06When to Use ToolSearchTool and Introduction to Programmatic Tool Calling

When to Use ToolSearchTool and Introduction to Programmatic Tool Calling

25:05Programmatic Tool Calling: Addressing Inference Overhead and Improving Accuracy

Programmatic Tool Calling: Addressing Inference Overhead and Improving Accuracy

28:04Python vs. TypeScript for Programmatic Tool Calling and Introduction to Tool Use Examples

Python vs. TypeScript for Programmatic Tool Calling and Introduction to Tool Use Examples

31:16Tool Use Examples and Best Practices for Combining Features

Tool Use Examples and Best Practices for Combining Features

34:04The Layers of Abstraction and the Need for Concise Tool Definitions

The Layers of Abstraction and the Need for Concise Tool Definitions

37:12The Need for Better Protocols and a Potential Acquisition of Zedd

The Need for Better Protocols and a Potential Acquisition of Zedd