Advanced Tool Use on the Claude Developer Platform
By Anthropic | Published Nov 24, 2025
We've added three new beta features that let Claude discover, learn, and execute tools dynamically.
The future of AI agents is one where models work seamlessly across hundreds or thousands of tools - IDE assistants, operations coordinators connecting Slack, GitHub, Jira, and dozens of MCP servers.
The Core Problems
Context pollution from tool definitions: 50+ MCP tools can consume 100K+ tokens before conversation starts
Inference overhead: Each tool call requires full model inference pass
Schema isn't enough: JSON Schema defines structure but not usage patterns
Three New Features
1. Tool Search Tool
Instead of loading all 55K tokens of tool definitions upfront, Claude discovers tools on-demand.
85% reduction in token usage
Only loads tools actually needed
Opus 4 accuracy improved: 49% → 74%
2. Programmatic Tool Calling (PTC)
Claude writes Python code to orchestrate tools instead of individual API round-trips.
37% token reduction on complex tasks
Eliminates 19+ inference passes for 20 tool calls
Only final results enter context, not intermediate data
Example: "Which team exceeded Q3 budget?"
Traditional: 2000+ expense line items in context
PTC: Only 3 names who exceeded budget in context
3. Tool Use Examples
Provide concrete usage examples in tool definitions.
Shows format conventions, ID patterns, correlations
Accuracy improved: 72% → 90% on complex parameters
Best Practices
Layer features strategically based on bottleneck
Keep 3-5 most-used tools always loaded
Document return formats for PTC
Use 1-5 realistic examples per tool
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