What is Programmatic Tool Calling?

It is a feature where Claude writes and executes sandboxed Python code to call tools. This allows intermediate data processing to happen outside the context window, reducing inference passes and token consumption.

What kind of accuracy improvements can I expect?

By implementing structured tool use examples, production systems have seen parameter handling accuracy increase from approximately 72% to 90%.

Can I use these features independently?

Yes. While they offer compounding benefits when used together, you can implement tool search, programmatic calling, or tool examples individually based on your specific system bottlenecks.

How does Tool Search save context tokens?

Tool Search uses regex or natural language queries to dynamically find and load only the specific tool definitions needed for a current request, rather than loading an entire library of tools into every prompt.

Is this skill compatible with MCP servers?

Absolutely. It is specifically designed to handle the scale of large MCP implementations where users may have hundreds or thousands of tools across multiple servers.

Claude Advanced Tool Use

Name: Claude Advanced Tool Use
Author: adaptationio

byadaptationio

API開発

Optimizes Claude's tool interactions through dynamic discovery, sandboxed programmatic execution, and high-accuracy usage patterns.

概要

This skill empowers Claude to handle massive toolsets and complex multi-step workflows with unprecedented efficiency. By implementing tool search for dynamic discovery of over 10,000 tools, programmatic tool calling to minimize context bloat during data processing, and detailed examples to boost parameter accuracy, developers can reduce token usage by up to 90% while improving system reliability. It is an essential capability for building production-grade agentic systems that require scalable tool orchestration, cost-effective performance, and high-precision parameter handling.

主な機能

0 GitHub stars
High-accuracy tool examples to eliminate parameter hallucinations
Deferred tool loading to minimize initial context window usage
Complex workflow orchestration with significant token reduction
Dynamic Tool Search (Regex & BM25) for 10,000+ tool scalability
Programmatic Tool Calling via sandboxed Python execution

ユースケース

Scaling AI agents to interact with massive Model Context Protocol (MCP) server libraries
Optimizing token costs and latency in high-volume production agent systems
Executing complex multi-step data aggregation and filtering tasks efficiently

概要

主な機能

0 GitHub stars
High-accuracy tool examples to eliminate parameter hallucinations
Deferred tool loading to minimize initial context window usage
Complex workflow orchestration with significant token reduction
Dynamic Tool Search (Regex & BM25) for 10,000+ tool scalability
Programmatic Tool Calling via sandboxed Python execution

ユースケース

Scaling AI agents to interact with massive Model Context Protocol (MCP) server libraries
Optimizing token costs and latency in high-volume production agent systems
Executing complex multi-step data aggregation and filtering tasks efficiently