agent-tool-builder
Helps your agent create tools to interact with the world. Well-designed tools prevent errors and save resources.
What it does
This agent helps create 'tools' so that other AIs can reliably interact with the world. It teaches how to design these tools correctly to avoid errors and unnecessary costs.
When to use it
- ✓When you want an AI to use an external tool to fetch information.
- ✓When defining how an AI should interact with a service or data.
- ✓To ensure that tools created for AIs work well and without failures.
Example of what to ask your AI
“How can I create a tool for the AI to tell me the weather in a city?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It teaches how to describe the tools that the AI will use, focusing on how to explain what the tool does and what information it needs. The description is more important than the code itself, as it's what the AI reads.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill agent-tool-builderOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/agent-tool-builder/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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 implementations. The LLM never sees your code - it only sees the schema and description.
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
Python Example
""" import anthropic from anthropic import beta_tool
client = anthropic.Anthropic()
@beta_tool def get_weather(location: str, unit: str = "fahrenheit") -> str: '''Get the current weather in a given location.
Args:
location: The city and state, e.g. San Francisco, CA
unit: Temperature unit, either 'celsius' or 'fahrenheit'
'''
# Implementation
return json.dumps({"temperature": "72°F", "conditions": "Sunny"})
@beta_tool def search_web(query: str) -> str: '''Search the web for information.
Args:
query: The search query
'''
# Implementation
return json.dumps({"results": [...]})
Tool runner handles the loop
runner = client.beta.messages.tool_runner( model="claude-sonnet-4-5", max_tokens=1024, tools=[get_weather, search_web], messages=[ {"role": "user", "content": "What's the weather in Paris?"} ] )
Process each message
for message in runner: print(message.content[0].text)
Or just get final result
final = runner.until_done() """
When to Use
- User mentions or implies: agent tool
- User mentions or implies: function calling
- User mentions or implies: tool schema
- User mentions or implies: tool design
- User mentions or implies: mcp server
- User mentions or implies: mcp tool
- User mentions or implies: tool use
- User mentions or implies: build tool for agent
- User mentions or implies: define function
- User mentions or implies: input_schema
- User mentions or implies: tool_use
- User mentions or implies: tool_result
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
FAQ
Why is the tool description more important than the code?
The AI doesn't see your code directly; it understands the tool through its description and 'schema' (how data is organized). A good description guides the AI correctly.
What is 'schema'?
It's like a map that defines the structure of the data a tool uses or returns, ensuring everything is organized and understood by the AI.
What does 'hallucinate' or 'fail silently' mean?
These are errors an AI might make. 'Hallucinate' is when the AI invents information. 'Fail silently' is when the tool doesn't work as expected, but the AI doesn't warn about the problem.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2069
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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