Open Source Claude Advanced Tool Use

We’ve added three new beta features that let Claude discover, learn, and execute tools dynamically. Here’s how they work.

The future of AI agents is one where models work seamlessly across hundreds or thousands of tools. An IDE assistant that integrates git operations, file manipulation, package managers, testing frameworks, and deployment pipelines. An operations coordinator that connects Slack, GitHub, Google Drive, Jira, company databases, and dozens of MCP servers simultaneously.

To build effective agents, they need to work with unlimited tool libraries without stuffing every definition into context upfront. Our blog article on using code execution with MCP discussed how tool results and definitions can sometimes consume 50,000+ tokens before an agent reads a request. Agents should discover and load tools on-demand, keeping only what's relevant for the current task.

Agents also need the ability to call tools from code. When using natural language tool calling, each invocation requires a full inference pass, and intermediate results pile up in context whether they're useful or not. Code is a natural fit for orchestration logic, such as loops, conditionals, and data transformations. Agents need the flexibility to choose between code execution and inference based on the task at hand.

Agents also need to learn correct tool usage from examples, not just schema definitions. JSON schemas define what's structurally valid, but can't express usage patterns: when to include optional parameters, which combinations make sense, or what conventions your API expects.

Today, we're releasing three features that make this possible:

In internal testing, we’ve found these features have helped us build things that wouldn’t have been possible with conventional tool use patterns. For example, Claude for Excel uses Programmatic Tool Calling to read and modify spreadsheets with thousands of rows without overloading the model’s context window.

Based on our experience, we believe these features open up new possibilities for what you can build with Claude.

MCP tool definitions provide important context, but as more servers connect, those tokens can add up. Consider a five-server setup:

That's 58 tools consuming approximately 55K tokens before the conversation even starts. Add more servers like Jira (which alone uses ~17K tokens) and you're quickly approaching 100K+ token overhead. At Anthropic, we've seen tool definitions consume 134K tokens before optimization.

Source: HackerNews