What is the core focus of the AI Agent Tool Designer skill?

The core focus is maximizing agent effectiveness by designing tools around human-like workflows and technical constraints like context window limits, rather than just exposing API endpoints.

Why are actionable error messages important for AI tools?

AI agents learn and iterate through feedback. Clear, descriptive error messages guide the agent toward correct usage patterns, allowing them to self-correct and succeed on retries.

How does this skill help with token management?

It provides strategies for returning high-signal information, implementing character limits (typically 25,000 chars), and using pagination to prevent agents from running out of context.

Can I use this for building Model Context Protocol (MCP) servers?

Yes. While specifically designed for any AI tool interface, these principles are foundational for creating high-quality MCP servers that agents can use reliably.

AI Agent Tool Designer

Name: AI Agent Tool Designer
Author: nityeshaga

bynityeshaga

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API Development

Provides architectural principles and implementation patterns for designing high-performance tools that AI agents can use effectively.

About

The AI Agent Tool Designer skill is a specialized guide for developers building custom agent systems or AI-accessible interfaces. It shifts the focus from simple API wrapping to agent-centric design, prioritizing high-impact workflows, context-window efficiency, and actionable error handling. By implementing these patterns—such as standardized JSON/Markdown outputs, pagination strategies, and natural task naming—developers can significantly increase the success rate of AI agents performing complex, real-world tasks within constrained environments.

Key Features

Workflow-centric tool consolidation patterns
Evaluation-driven development methodologies for agent tools
Actionable, agent-friendly error message design
Context window and token optimization strategies
Standardized JSON and Markdown response guidelines
47 GitHub stars

Use Cases

Optimizing API responses to reduce token consumption and improve reasoning
Designing custom internal toolsets for proprietary agent systems
Building robust error handling that enables AI agents to self-correct

About

Key Features

Workflow-centric tool consolidation patterns
Evaluation-driven development methodologies for agent tools
Actionable, agent-friendly error message design
Context window and token optimization strategies
Standardized JSON and Markdown response guidelines
47 GitHub stars

Use Cases

Optimizing API responses to reduce token consumption and improve reasoning
Designing custom internal toolsets for proprietary agent systems
Building robust error handling that enables AI agents to self-correct