sickn33/agentic-awesome-skills2 files

Agent Tool Builder

Tools are how AI agents interact with the world. A well-designed

Specification
Skill ID
sickn33/agentic-awesome-skills/agent-tool-builder
Publisher
sickn33
Repository
agentic-awesome-skills
Installs
0
Files
2
Synced
Sep 23, 2026
How to use it

Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.

sickn33/agentic-awesome-skills/agent-tool-builderInstalls these files
  • SKILL.md
  • references/detailed-guide.md

What this skill tells the agent

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.

Use this skill in a project

Open any RiverX project, search the Skills panel for sickn33/agentic-awesome-skills/agent-tool-builder, and install it in one click.

Use this skill