Agent Tool Builder

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.

Principles

• Description quality > implementation quality for LLM accuracy
• Aim for fewer than 20 tools - more causes confusion
• Every tool needs explicit error handling - silent failures poison agents
• Return strings, not objects - LLMs process text
• Validation gates before execution - reject, fix, or escalate, never silent fail
• Test tools with the LLM, not just unit tests

Capabilities

• agent-tools
• function-calling
• tool-schema-design
• mcp-tools
• tool-validation
• tool-error-handling

Scope

• multi-agent-coordination → multi-agent-orchestration
• agent-memory → agent-memory-systems
• api-design → api-designer
• llm-prompting → prompt-engineering

Tooling

Standards

• JSON Schema - When: All tool definitions Note: The universal format for tool schemas
• MCP (Model Context Protocol) - When: Building reusable, cross-platform tools Note: Anthropic's open standard, widely adopted

Frameworks

• Anthropic SDK - When: Claude-based agents Note: Beta tool runner handles most complexity
• OpenAI Functions - When: OpenAI-based agents Note: Use strict mode for guaranteed schema compliance
• Vercel AI SDK - When: Multi-provider tool handling Note: Abstracts differences between providers
• LangChain Tools - When: LangChain-based agents Note: Converts MCP tools to LangChain format

Patterns

Tool Schema Design

Creating clear, unambiguous JSON Schema for tools

When to use: Defining any new tool for an agent

TOOL SCHEMA BEST PRACTICES:

1. Detailed Descriptions (Most Important)

""" BAD - Too vague: { "name": "getstockprice", "description": "Gets stock price", "input_schema": { "type": "object", "properties": { "ticker": {"type": "string"} } } }

GOOD - Comprehensive: { "name": "getstockprice", "description": "Retrieves the current stock price for a given ticker symbol. The ticker symbol must be a valid symbol for a publicly traded company on a major US stock exchange like NYSE or NASDAQ. Returns the latest trade price in USD. Use when the user asks about current or recent stock prices. Does NOT provide historical data, company info, or predictions.", "input_schema": { "type": "object", "properties": { "ticker": { "type": "string", "description": "The stock ticker symbol, e.g. AAPL for Apple Inc." } }, "required": ["ticker"] } } """

2. Parameter Descriptions

""" Every parameter needs:

• What it is
• Format expected
• Example value
• Edge cases/limitations

{ "location": { "type": "string", "description": "City and state/country. Format: 'City, State' for US (e.g., 'San Francisco, CA') or 'City, Country' for international (e.g., 'Tokyo, Japan'). Do not use ZIP codes or coordinates." }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"], "description": "Temperature unit. Defaults to user's locale if not specified. Use 'fahrenheit' for US users, 'celsius' for others." } } """

3. Use Enums When Possible

""" Enums constrain the LLM to valid values:

"priority": { "type": "string", "enum": ["low", "medium", "high", "critical"], "description": "Task priority level" }

"action": { "type": "string", "enum": ["create", "read", "update", "delete"], "description": "The CRUD operation to perform" } """

4. Required vs Optional

""" Be explicit about what's required:

{ "type": "object", "properties": { "query": {...}, // Required "limit": {...}, // Optional with default "offset": {...} // Optional }, "required": ["query"], "additionalProperties": false // Strict mode } """

Tool with Input Examples

Using examples to guide LLM tool usage

When to use: Complex tools with nested objects or format-sensitive inputs

TOOL USE EXAMPLES (Anthropic Beta Feature):

""" Examples show Claude concrete patterns that schemas can't express. Improves accuracy from 72% to 90% on complex operations. """

{ "name": "createcalendarevent", "description": "Creates a calendar event with optional attendees and reminders", "inputschema": { "type": "object", "properties": { "title": {"type": "string", "description": "Event title"}, "starttime": { "type": "string", "description": "ISO 8601 datetime, e.g. 2024-03-15T14:00:00Z" }, "durationminutes": {"type": "integer", "description": "Event duration"}, "attendees": { "type": "array", "items": {"type": "string"}, "description": "Email addresses of attendees" } }, "required": ["title", "starttime", "durationminutes"] }, "inputexamples": [ { "title": "Team Standup", "starttime": "2024-03-15T09:00:00Z", "durationminutes": 30, "attendees": ["alice@company.com", "bob@company.com"] }, { "title": "Quick Chat", "starttime": "2024-03-15T14:00:00Z", "durationminutes": 15 }, { "title": "Project Review", "starttime": "2024-03-15T16:00:00-05:00", "durationminutes": 60, "attendees": ["team@company.com"] } ] }

EXAMPLE DESIGN PRINCIPLES:

- Use realistic data, not placeholders

- Show minimal, partial, and full specification patterns

- Keep concise: 1-5 examples per tool

- Focus on ambiguous cases

Tool Error Handling

Returning errors that help the LLM recover

When to use: Any tool that can fail

ERROR HANDLING BEST PRACTICES:

Return Informative Errors

""" BAD: {"error": "Failed"} {"error": true}

GOOD: { "error": true, "errortype": "notfound", "message": "Location 'Atlantis' not found in weather database. Please provide a real city name like 'San Francisco, CA'.", "suggestions": ["San Francisco, CA", "Los Angeles, CA"] } """

Anthropic Tool Result with Error

""" { "type": "toolresult", "tooluseid": "toolu01A09q90qw90lq917835lq9", "content": "Error: Location 'Atlantis' not found in weather database. Please provide a real city name like 'San Francisco, CA'.", "is_error": true } """

Error Categories to Handle

"""

1. Input Validation Errors

• Missing required parameters
• Invalid format
• Out of range values

1. External Service Errors

• API unavailable
• Rate limited
• Timeout

1. Business Logic Errors

• Resource not found
• Permission denied
• Conflict/duplicate

1. Internal Errors

• Unexpected exceptions
• Data corruption

"""

Implementation Pattern

""" from dataclasses import dataclass from typing import Union

@dataclass class ToolResult: success: bool content: str error_type: str = None suggestions: list[str] = None

def toresponse(self) -> dict: if self.success: return {"content": self.content} return { "content": f"Error ({self.errortype}): {self.content}", "is_error": True }

def getweather(location: str) -> ToolResult: # Validate input if not location or len(location) ({ tools: [ { name: "getweather", description: "Get current weather for a location. Returns temperature, conditions, and humidity. Use for weather queries about specific cities.", inputSchema: { type: "object", properties: { location: { type: "string", description: "City and state, e.g. 'San Francisco, CA'" }, unit: { type: "string", enum: ["celsius", "fahrenheit"], default: "fahrenheit" } }, required: ["location"] } } ] }));

// Handle tool calls server.setRequestHandler("tools/call", async (request) => { const { name, arguments: args } = request.params;

if (name === "get_weather") { try { const weather = await fetchWeather(args.location, args.unit); return { content: [ { type: "text", text: JSON.stringify(weather) } ] }; } catch (error) { return { content: [ { type: "text", text: Error: ${error.message} } ], isError: true }; } }

throw new Error(Unknown tool: ${name}); });

// Start server const transport = new StdioServerTransport(); await server.connect(transport); """

MCP Benefits

"""

• Universal compatibility across LLM providers
• Reusable tool libraries
• Streaming and SSE transport support
• Built-in observability
• Tool access controls

"""

Tool Runner Pattern

Using SDK tool runners for automatic handling

When to use: Building tool loops without manual management

TOOL RUNNER (Anthropic SDK Beta):

""" The tool runner handles the tool call loop automatically:

• Executes tools when Claude calls them
• Manages conversation state
• Handles error retries
• Provides streaming support

"""

Python Example

""" import anthropic from anthropic import beta_tool

client = anthropic.Anthropic()

@betatool def getweather(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"})

@betatool def searchweb(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.toolrunner( model="claude-sonnet-4-5", maxtokens=1024, tools=[getweather, searchweb], 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() """

TypeScript with Zod

""" import { Anthropic } from '@anthropic-ai/sdk'; import { betaZodTool } from '@anthropic-ai/sdk/helpers/beta/zod'; import { z } from 'zod';

const anthropic = new Anthropic();

const getWeatherTool = betaZodTool({ name: 'get_weather', description: 'Get the current weather in a given location', inputSchema: z.object({ location: z.string().describe('City and state, e.g. San Francisco, CA'), unit: z.enum(['celsius', 'fahrenheit']).default('fahrenheit') }), run: async (input) => { // Type-safe input! return JSON.stringify({temperature: '72°F'}); } });

const runner = anthropic.beta.messages.toolRunner({ model: 'claude-sonnet-4-5', max_tokens: 1024, tools: [getWeatherTool], messages: [{ role: 'user', content: "What's the weather in Paris?" }] });

for await (const message of runner) { console.log(message.content[0].text); } """

Parallel Tool Execution

Running multiple tools simultaneously

When to use: Independent tool calls that can run in parallel

PARALLEL TOOL EXECUTION:

""" By default, Claude can call multiple tools in one response. This dramatically reduces latency for independent operations. """

Handling Parallel Results

"""

Claude returns multiple tool_use blocks:

response.content = [ {"type": "text", "text": "I'll check both locations..."}, {"type": "tooluse", "id": "toolu01", "name": "getweather", "input": {"location": "San Francisco, CA"}}, {"type": "tooluse", "id": "toolu02", "name": "getweather", "input": {"location": "New York, NY"}}, {"type": "tooluse", "id": "toolu03", "name": "gettime", "input": {"timezone": "America/LosAngeles"}}, {"type": "tooluse", "id": "toolu04", "name": "gettime", "input": {"timezone": "America/NewYork"}} ]

Execute in parallel

import asyncio

async def executetoolsparallel(tooluses): tasks = [executetool(t) for t in tool_uses] return await asyncio.gather(*tasks)

results = await executetoolsparallel(tool_uses)

Return ALL results in SINGLE user message (critical!)

toolresults = [ {"type": "toolresult", "tooluseid": "toolu01", "content": "72°F, Sunny"}, {"type": "toolresult", "tooluseid": "toolu02", "content": "45°F, Cloudy"}, {"type": "toolresult", "tooluseid": "toolu03", "content": "2:30 PM PST"}, {"type": "toolresult", "tooluseid": "toolu_04", "content": "5:30 PM EST"} ]

CORRECT: All results in one message

messages.append({"role": "user", "content": tool_results})

WRONG: Separate messages (breaks parallel execution pattern)

messages.append({"role": "user", "content": [tool_results[0]]})

messages.append({"role": "user", "content": [tool_results[1]]})

"""

Encouraging Parallel Tool Use

""" Add to system prompt: "For maximum efficiency, whenever you need to perform multiple independent operations, invoke all relevant tools simultaneously rather than sequentially." """

Disabling Parallel (When Needed)

""" response = client.messages.create( model="claude-sonnet-4-5", tools=tools, toolchoice={"type": "auto", "disableparalleltooluse": True}, messages=messages ) """

Validation Checks

Tool Description Must Be Comprehensive

Severity: WARNING

Tool descriptions should be at least 100 characters

Message: Tool description is too short. Add details about when to use it, parameters, and return values.

Parameter Descriptions Required

Severity: WARNING

Every parameter should have a description

Message: Parameter missing description. Describe what it is and the expected format.

Schema Should Specify Required Fields

Severity: INFO

Explicitly define which fields are required

Message: Schema doesn't specify required fields. Add 'required' array.

Tool Implementation Needs Error Handling

Severity: ERROR

Tool functions should handle exceptions

Message: Tool function without try/except block. Add error handling.

Error Results Need is_error Flag

Severity: WARNING

When returning errors, set is_error to true

Message: Error result without iserror flag. Add 'iserror': true.

Tools Should Return Strings

Severity: WARNING

Return JSON string, not dict/object

Message: Returning dict instead of string. Use json.dumps() or JSON.stringify().

Tools Should Validate Inputs

Severity: WARNING

Validate LLM-provided inputs before execution

Message: Tool function without visible input validation. Validate before execution.

SQL Queries Must Use Parameterization

Severity: ERROR

Never concatenate user input into SQL

Message: SQL query appears to use string concatenation. Use parameterized queries.

External Calls Need Timeouts

Severity: WARNING

HTTP requests and external calls should have timeouts

Message: External API call without timeout. Add timeout parameter.

MCP Tools Must Have Input Schema

Severity: ERROR

All MCP tools require inputSchema

Message: MCP tool definition missing inputSchema.

Collaboration

Delegation Triggers

• user needs to coordinate multiple tools -> multi-agent-orchestration (Tool orchestration across agents)
• user needs persistent memory between tool calls -> agent-memory-systems (State management for tools)
• user building voice agent tools -> voice-agents (Audio/voice-specific tool requirements)
• user needs computer control tools -> computer-use-ag

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sickn33
• Source: sickn33/antigravity-awesome-skills
• License: MIT
• Homepage: https://sickn33.github.io/antigravity-awesome-skills/

Install and usage instructions live in the source repository linked above.