Guide · Agent skill
How to use the Agent Tool Builder agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
Agent Tool Builder is a skill for designing the tools that AI models call to act on the world, such as functions that look up data or run actions. It is written for developers who build function calling or tool use into an agent, and for teams writing MCP servers who need consistent tool schemas. After following this guide you will know how to install the skill, how to trigger it with a request, and what it will help you check in your tool designs.
Agent Tool Buildersickn33/agentic-awesome-skills · MIT Download the skill Quick start
- 1Download the ZIP. Get the skill ZIP from this page. It contains SKILL.md and references/detailed-guide.md.
- 2Unzip into a skills folder. Extract it to ~/.claude/skills/ for personal use, or to .claude/skills/ in a project.
- 3Or upload in the Claude apps. Go to Settings → Capabilities → Skills and upload the skill.
- 4Ask for tool design help. Describe the tool or agent problem in plain language, mentioning terms like tool schema or function calling.
- 5Review the output against your system. Check schemas, descriptions, validation and error handling against your real use case.
- 6Enforce safety in code. Add authorization and approval controls in your implementation before any tool triggers a real external effect.
- 7Test with the model. Run realistic requests through the LLM to confirm it selects the right tools.
What the skill makes the agent do
When the skill is active, the agent gets guidance on tool design from schema to error handling. The source covers JSON Schema best practices, writing descriptions that help the model choose the right tool, validation, and the MCP (Model Context Protocol) standard.
The skill's core point is that descriptions guide which tool the model selects, but the implementation must enforce correctness, authorization and safety. Both need testing, and the model may also inspect exposed code and results.
- Design tool schemas and input parameters (for example, input_schema)
- Write descriptions that help the model pick the right tool
- Add input validation and explicit error handling so failures are not silent
- Follow the MCP standard for tool definitions
Install the skill
Download the ZIP for this skill from this page. It contains SKILL.md and references/detailed-guide.md. Unzip it into your personal skills folder ~/.claude/skills/, or into a project's .claude/skills/ folder if you want it limited to one project.
Alternatively, in the Claude apps, go to Settings → Capabilities → Skills and upload the skill there.
- Personal: ~/.claude/skills/
- Project: .claude/skills/ inside your project
- Claude apps: Settings → Capabilities → Skills
Trigger the skill with natural requests
You do not need a special command. Ask for tool design work in plain language. The SKILL.md lists phrases that should activate it, including agent tool, function calling, tool schema, tool design, MCP server, tool use, and input_schema.
Be specific about your goal, such as which tool the agent needs and what it should return, so the guidance matches your situation.
- "Design a tool schema for an agent that looks up order status."
- "My agent keeps picking the wrong tool. Review my tool descriptions."
- "Help me define an MCP server with consistent tool schemas."
- "My tool call fails silently. Add proper error handling to this tool_result flow."
Use the Python example (optional)
The SKILL.md includes a historical Python sketch using Anthropic's SDK with the @beta_tool decorator and a tool runner. The tools are mocks: the weather result is fixed, synthetic data, not a real forecast, and the search tool returns an empty list marked as mock.
Before running it, the source says to pin and verify the SDK beta API and a model your account can access, since billed calls are involved. The example requires Python, the anthropic SDK, and an Anthropic API key. The guidance itself is tool-agnostic and does not need the SDK.
- Model used in the sketch: claude-sonnet-4-5 (verify your account can access it)
- The tool runner does not authorize external effects; your code must do that
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?"}]
)Safety notes before you connect real systems
This skill runs no scripts and makes no network calls itself. The source marks its risk as critical because the tools you design may trigger real external effects. Descriptions do not enforce safety; authorization and safety must be enforced in code.
Read the detailed guide before executing the skill. The source treats its safety, prerequisites and validation requirements as mandatory. Tools need bounded execution and approval policies.
- Enforce authorization and approval in your implementation, not in descriptions
- Validate inputs and reject, fix or escalate bad calls rather than failing silently
- Test tools with the LLM, not only with unit tests
Tips for better tool design
The detailed guide advises choosing a discoverable, relevant subset of tools for each task rather than exposing everything. It also recommends returning provider- or protocol-defined content and structured results, with JSON text being one option.
If the skill's scope does not fit, the guide points to related skills for multi-agent orchestration, agent memory, API design and prompting.
- Keep the tool set relevant to the task; the guide sets no universal 20-tool limit
- Give every tool explicit error handling
- Check that the model picks the right tool in realistic runs
What people use it for
- Designing input_schema definitions for a tool that looks up customer data
- Rewriting vague tool descriptions so a model picks the correct tool
- Debugging an agent that fails silently when a tool returns an error
- Defining consistent tool schemas across a team's MCP servers
- Reviewing a mock Python tool-use example before connecting it to real services
Frequently asked questions
Is the Agent Tool Builder skill free?
The skill is licensed under MIT, as stated for this item. Check the repository license file for the full terms before reuse.
What do I need to use the agent-tool-builder skill?
The guidance itself is tool-agnostic and needs no special software. The optional Python example requires Python, the anthropic SDK, and an Anthropic API key with access to a beta SDK tool runner and a model.
How do I install agent-tool-builder?
Download the ZIP from this page and unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder. You can also upload it in the Claude apps under Settings → Capabilities → Skills.
Does this skill run scripts or use the network?
According to the listing, it runs no scripts and uses no network. It provides design guidance only.
Is this skill enough to connect an agent to production systems?
No. The skill does not enforce authorization or approval controls, so you must build them into your code. Descriptions alone do not protect external actions.
What is the difference between this skill and an MCP server?
An MCP server is an implementation that exposes tools. This skill is design guidance for writing those tool schemas and descriptions, and it references the MCP standard; it is not a ready-made server.
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Our verdict
Use this skill if you are a developer or team designing function calling, tool use or MCP tool schemas and need help with descriptions, validation and error handling. Skip it if you want a finished tool or app, or if you plan to let agents trigger real external actions without your own approval and authorization controls.
Download the skill