agent-tool-builder
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
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Agent Tool Builder - AI Tool Design Expert
Skill Overview
Agent Tool Builder focuses on helping developers design AI tool interfaces that LLMs can understand and call precisely, covering schema design, description writing, error handling, and MCP protocol best practices.
Applicable Scenarios
When you need to create callable tools for Claude, GPT-4, or other LLMs, this skill provides a complete design solution from JSON Schema definitions to error handling, ensuring the tool is called correctly.
When your agent exhibits hallucinations, calls the wrong tool, or experiences silent failures, this skill helps diagnose the root cause — often tool descriptions or schema design issues rather than implementation code problems.
When you need to convert existing tools to the MCP (Model Context Protocol) standard format, or design new tools from scratch that comply with the MCP specification, it provides protocol guidance and schema generation recommendations.
Core Capabilities
Generate clear, unambiguous JSON Schema definitions, including parameter types, enum values, required fields, and default value settings. Core principle: the more explicit the schema constraints, the more accurate the LLM calls.
Write descriptions that help the LLM understand the tool's purpose, parameter meanings, and return format. Key insight: LLMs never look at your code; they only look at the schema and description.
Design error response formats that help the LLM recover from errors, including error types, fixable suggestions, and retry guidance, to avoid the agent entering a dead loop.
Frequently Asked Questions
Why does the LLM always call the wrong tool?
Usually the problem isn't the implementation code but the tool descriptions and schema design. Check: does the description clarify the tool's boundaries? Do the parameter explanations include examples? Are the enum values complete? This skill can help you diagnose and fix these issues.
What is the MCP protocol? Do I need to use it?
MCP (Model Context Protocol) is an open specification that is becoming a standard protocol for AI tools. It unifies tool definition formats, allowing tools to be reused across different agent frameworks. If your tool needs to be used cross-platform, adopting the MCP standard is recommended.
How long should a tool description be?
The description should be detailed enough for the LLM to understand, but not verbose. Suggested structure: tool purpose + use cases + important constraints + return format description. Parameter descriptions should include type, unit, allowed range, and examples.