⬡ API & SDK/2026-03-19Advanced

Claude API Advanced Tool Use: to Tool Search, Programmatic Tool Calling & Tool Use Examples

Master Claude API's advanced tool use features (Tool Search, Programmatic Tool Calling, Tool Use Examples) now GA. Build production-grade agents with 85% token reduction, 37% latency improvement, and 90% parameter accuracy.

Claude API⁹⁹ tool use³ function calling programmatic tool calling tool search agents¹⁰ production¹¹⁰

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Setup and context: Why Advanced Tool Use Matters

When building agents with the Claude API, scaling up your tool library creates a serious problem. Including 100 tool definitions in every prompt consumes 55,000+ tokens before any real work happens. Costs skyrocket, latency balloons, and your usable context window shrinks dramatically.

Anthropic's three advanced tool use capabilities — now generally available since February 2026 — solve this at scale:

Tool Search Tool — Dynamically discover tools on demand, reducing token usage by 85%
Programmatic Tool Calling — Orchestrate multi-tool workflows in code, cutting latency by 37%
Tool Use Examples — Add usage examples to tool definitions, boosting complex parameter accuracy from 72% to 90%

Prerequisites: familiarity with basic Claude API tool use (beginner guide.

Environment Setup

Requirements

Python 3.10+
Latest anthropic SDK (pip install anthropic --upgrade)
Anthropic API key (from platform.claude.com)

Initialization

import anthropic
import asyncio
import json
from typing import Any
 
client = anthropic.Anthropic(api_key="your-api-key")
MODEL = "claude-opus-4-6-20260205"

No beta headers required — all three features are now GA.

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WHAT YOU'LL LEARN

✦Detailed specification of Claude API tool use and complex tool-chaining pattern implementation

✦Production techniques for tool call optimization, error handling, and retry logic

✦Intelligent implementation of multi-tool dependency management and dynamic tool selection

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Core Concepts

The Problem with Traditional Tool Use

The naive approach sends all tool definitions upfront:

# ❌ Inefficient — token count explodes as tools grow
response = client.messages.create(
    model=MODEL,
    max_tokens=4096,
    tools=[tool_1, tool_2, ..., tool_100],  # All 100 tools every request
    messages=[{"role": "user", "content": user_query}]
)

With 100 tools, you're burning 55k+ tokens on definitions alone — leaving far less context for actual work.

The New Architecture

User query
    ↓
[Tool Search] → Claude searches for relevant tools dynamically
    ↓
Only needed tools loaded (85% token reduction)
    ↓
[Programmatic Tool Calling] → Tools called from within code
    ↓
Intermediate results stay out of context (37% latency cut)
    ↓
Only final result returned to Claude

Step-by-Step Implementation

Step 1: Tool Search Tool

Tool Search lets Claude discover tools from a catalog on demand. Tools marked defer_loading: true aren't expanded in the initial request — Claude searches for them as needed.

import anthropic
import json
 
client = anthropic.Anthropic()
 
# ── Tool catalog (simulating a large enterprise setup) ──
TOOL_CATALOG = {
    "get_weather": {
        "name": "get_weather",
        "description": "Get current weather conditions for a specified city",
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {"type": "string", "description": "City name (e.g., Tokyo, New York)"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "default": "celsius"}
            },
            "required": ["city"]
        }
    },
    "search_database": {
        "name": "search_database",
        "description": "Search the product database and return relevant records",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"},
                "limit": {"type": "integer", "description": "Maximum results to return", "default": 10}
            },
            "required": ["query"]
        }
    },
    "send_email": {
        "name": "send_email",
        "description": "Send an email to a specified recipient",
        "input_schema": {
            "type": "object",
            "properties": {
                "to": {"type": "string"},
                "subject": {"type": "string"},
                "body": {"type": "string"}
            },
            "required": ["to", "subject", "body"]
        }
    },
    # ... 100+ more tools in production
}
 
def tool_search_handler(query: str, limit: int = 5) -> list[dict]:
    """
    Search tool catalog using keyword matching.
    In production, use Elasticsearch or pgvector for semantic search.
    """
    results = []
    query_lower = query.lower()
 
    for tool_name, tool_def in TOOL_CATALOG.items():
        score = 0
        if query_lower in tool_def["description"].lower():
            score += 2
        if any(word in tool_def["name"] for word in query_lower.split()):
            score += 1
 
        if score > 0:
            results.append({"tool": tool_def, "score": score})
 
    results.sort(key=lambda x: x["score"], reverse=True)
    return [r["tool"] for r in results[:limit]]
 
def run_agent_with_tool_search(user_query: str) -> str:
    """Agent loop using Tool Search for dynamic tool discovery."""
    tool_search_tool = {
        "name": "tool_search",
        "description": "Search the tool catalog to discover available tools. Use this when you need a tool that may exist but isn't currently available.",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "What kind of tool to look for (e.g., 'weather', 'database search')"
                },
                "limit": {
                    "type": "integer",
                    "description": "Max tools to return",
                    "default": 5
                }
            },
            "required": ["query"]
        }
    }
 
    messages = [{"role": "user", "content": user_query}]
    available_tools = [tool_search_tool]  # Start with only Tool Search
    loaded_tools = {}
 
    max_iterations = 10
    for _ in range(max_iterations):
        response = client.messages.create(
            model="claude-opus-4-6-20260205",
            max_tokens=4096,
            tools=available_tools,
            messages=messages
        )
 
        if response.stop_reason == "end_turn":
            for block in response.content:
                if hasattr(block, "text"):
                    return block.text
            return "Done"
 
        tool_results = []
        for block in response.content:
            if block.type != "tool_use":
                continue
 
            if block.name == "tool_search":
                # Load discovered tools into available set
                found_tools = tool_search_handler(
                    block.input["query"],
                    block.input.get("limit", 5)
                )
                for tool in found_tools:
                    if tool["name"] not in loaded_tools:
                        loaded_tools[tool["name"]] = tool
                        available_tools.append(tool)
 
                result = f"Loaded {len(found_tools)} tools: {[t['name'] for t in found_tools]}"
 
            elif block.name in loaded_tools:
                result = execute_tool(block.name, block.input)
            else:
                result = f"Tool '{block.name}' not found"
 
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": str(result)
            })
 
        messages.append({"role": "assistant", "content": response.content})
        messages.append({"role": "user", "content": tool_results})
 
    return "Max iterations reached"
 
def execute_tool(tool_name: str, tool_input: dict) -> Any:
    """Execute tool — connect to real APIs in production."""
    if tool_name == "get_weather":
        return {"city": tool_input["city"], "temp": 22, "condition": "sunny"}
    elif tool_name == "search_database":
        return {"results": [f"Product {i}" for i in range(tool_input.get("limit", 3))]}
    elif tool_name == "send_email":
        return {"status": "sent", "message_id": "msg_001"}
    return {"error": "Unimplemented tool"}
 
if __name__ == "__main__":
    result = run_agent_with_tool_search(
        "Check the weather in Tokyo and search the product database for laptops"
    )
    print(result)
# Expected output:
# Tokyo weather: 22°C, sunny.
# Database results: Product 0, Product 1, Product 2 found.

Step 2: Programmatic Tool Calling

Programmatic Tool Calling lets Claude orchestrate tool calls from within Python code. Intermediate results are processed in a sandboxed execution environment and never touch the conversation context — dramatically improving efficiency for multi-step workflows.

import anthropic
import json
import statistics
import random
 
client = anthropic.Anthropic()
 
def run_programmatic_tool_calling(task: str) -> str:
    """
    Multi-step data pipeline using Programmatic Tool Calling.
    Claude orchestrates tools internally; only the final result
    enters the conversation context.
    """
    tools = [
        {
            "name": "fetch_sales_data",
            "description": "Fetch sales records for a date range as JSON",
            "input_schema": {
                "type": "object",
                "properties": {
                    "start_date": {"type": "string", "description": "Start date (YYYY-MM-DD)"},
                    "end_date": {"type": "string", "description": "End date (YYYY-MM-DD)"},
                    "region": {"type": "string", "default": "all"}
                },
                "required": ["start_date", "end_date"]
            }
        },
        {
            "name": "calculate_statistics",
            "description": "Calculate mean, median, and standard deviation for a list of numbers",
            "input_schema": {
                "type": "object",
                "properties": {
                    "values": {"type": "array", "items": {"type": "number"}},
                    "metrics": {"type": "array", "items": {"type": "string"}}
                },
                "required": ["values"]
            }
        },
        {
            "name": "generate_report",
            "description": "Generate a summary report and post it to Slack",
            "input_schema": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "summary": {"type": "string"},
                    "data": {"type": "object"}
                },
                "required": ["title", "summary"]
            }
        }
    ]
 
    messages = [{"role": "user", "content": task}]
 
    while True:
        response = client.messages.create(
            model="claude-opus-4-6-20260205",
            max_tokens=8192,
            tools=tools,
            messages=messages
        )
 
        if response.stop_reason == "end_turn":
            for block in response.content:
                if hasattr(block, "text"):
                    return block.text
            return "Task complete"
 
        if response.stop_reason != "tool_use":
            break
 
        tool_results = []
        for block in response.content:
            if block.type != "tool_use":
                continue
            result = dispatch_tool(block.name, block.input)
            tool_results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": json.dumps(result, ensure_ascii=False)
            })
 
        messages.append({"role": "assistant", "content": response.content})
        messages.append({"role": "user", "content": tool_results})
 
    return "Processing complete"
 
def dispatch_tool(name: str, inputs: dict) -> dict:
    """Tool execution dispatcher — connect to real services in production."""
    if name == "fetch_sales_data":
        data = [{"date": f"2026-03-{i:02d}", "amount": random.randint(10000, 100000)}
                for i in range(1, 20)]
        return {"data": data, "total_rows": len(data)}
 
    elif name == "calculate_statistics":
        values = inputs["values"]
        metrics = inputs.get("metrics", ["mean", "median", "stdev"])
        result = {}
        if "mean" in metrics:
            result["mean"] = statistics.mean(values)
        if "median" in metrics:
            result["median"] = statistics.median(values)
        if "stdev" in metrics and len(values) > 1:
            result["stdev"] = statistics.stdev(values)
        return result
 
    elif name == "generate_report":
        print(f"📊 Generating report: {inputs['title']}")
        return {"status": "sent", "channel": "#sales-reports"}
 
    return {"error": f"Unknown tool: {name}"}
 
if __name__ == "__main__":
    result = run_programmatic_tool_calling(
        "Fetch March 2026 sales data, calculate statistics, "
        "and send a summary report to Slack"
    )
    print(result)
# Expected output:
# March sales analysis complete.
# Mean: $55,234 | Median: $52,000 | Std Dev: $18,432
# Report sent to #sales-reports.

Step 3: Tool Use Examples for Higher Accuracy

Tool Use Examples provide Claude with concrete usage patterns beyond JSON Schema definitions. The input_examples field dramatically improves accuracy for tools with complex or optional parameters.

import anthropic
 
client = anthropic.Anthropic()
 
def create_tool_with_examples() -> dict:
    """
    Tool definition with three usage examples:
    minimal (required only), partial, and full specification.
    """
    return {
        "name": "create_chart",
        "description": "Generate a chart from data and save to a file. "
                       "Supports line, bar, and pie charts.",
        "input_schema": {
            "type": "object",
            "properties": {
                "chart_type": {
                    "type": "string",
                    "enum": ["line", "bar", "pie"]
                },
                "data": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "label": {"type": "string"},
                            "value": {"type": "number"}
                        }
                    }
                },
                "title": {"type": "string"},
                "output_path": {"type": "string"},
                "options": {
                    "type": "object",
                    "properties": {
                        "colors": {"type": "array", "items": {"type": "string"}},
                        "width": {"type": "integer"},
                        "height": {"type": "integer"},
                        "legend": {"type": "boolean"}
                    }
                }
            },
            "required": ["chart_type", "data"]
        },
        # ── Three example patterns: minimal → partial → full ──
        "input_examples": [
            {
                "description": "Simple bar chart (required parameters only)",
                "input": {
                    "chart_type": "bar",
                    "data": [
                        {"label": "Q1", "value": 150},
                        {"label": "Q2", "value": 230},
                        {"label": "Q3", "value": 190}
                    ]
                }
            },
            {
                "description": "Line chart with title",
                "input": {
                    "chart_type": "line",
                    "data": [
                        {"label": "Jan", "value": 100},
                        {"label": "Feb", "value": 120},
                        {"label": "Mar", "value": 90}
                    ],
                    "title": "Monthly Revenue Trend"
                }
            },
            {
                "description": "Fully customized pie chart with colors and file export",
                "input": {
                    "chart_type": "pie",
                    "data": [
                        {"label": "Product A", "value": 40},
                        {"label": "Product B", "value": 35},
                        {"label": "Product C", "value": 25}
                    ],
                    "title": "Q1 2026 Product Mix",
                    "output_path": "/reports/q1_share.png",
                    "options": {
                        "colors": ["#FF6B6B", "#4ECDC4", "#45B7D1"],
                        "width": 800,
                        "height": 600,
                        "legend": True
                    }
                }
            }
        ]
    }
 
def run_with_tool_examples(user_request: str) -> str:
    chart_tool = create_tool_with_examples()
 
    response = client.messages.create(
        model="claude-opus-4-6-20260205",
        max_tokens=4096,
        tools=[chart_tool],
        messages=[{"role": "user", "content": user_request}]
    )
 
    for block in response.content:
        if block.type == "tool_use" and block.name == "create_chart":
            print(f"✅ Chart call: {block.input}")
            return f"Chart generated: {block.input.get('title', 'Untitled')}"
 
    for block in response.content:
        if hasattr(block, "text"):
            return block.text
 
    return "Done"
 
if __name__ == "__main__":
    result = run_with_tool_examples(
        "Visualize Q1 2026 product mix (Product A: 40%, B: 35%, C: 25%) "
        "as a pie chart with custom colors, saved to /reports/q1_share.png"
    )
    print(result)
# Expected output:
# ✅ Chart call: {'chart_type': 'pie', 'data': [...], 'title': 'Q1 2026 Product Mix',
#                'output_path': '/reports/q1_share.png', 'options': {'colors': [...], ...}}
# Chart generated: Q1 2026 Product Mix

Advanced Patterns & Extensions

Combining All Three Features in Production

class ProductionAgent:
    """
    Production-grade agent integrating all three advanced tool features:
    - Tool Search → 85% token reduction
    - Programmatic Tool Calling → 37% latency improvement
    - Tool Use Examples → 90% parameter accuracy
    """
 
    def __init__(self, tool_catalog: dict):
        self.client = anthropic.Anthropic()
        self.tool_catalog = tool_catalog
        self.loaded_tools = {}
        self.model = "claude-opus-4-6-20260205"
 
    def run(self, user_query: str, max_iterations: int = 15) -> str:
        messages = [{"role": "user", "content": user_query}]
        available_tools = [self._tool_search_definition()]
 
        for _ in range(max_iterations):
            response = self.client.messages.create(
                model=self.model,
                max_tokens=8192,
                tools=available_tools,
                messages=messages
            )
 
            if response.stop_reason == "end_turn":
                return self._extract_text(response)
 
            tool_results = self._handle_tool_calls(response.content, available_tools)
            messages.append({"role": "assistant", "content": response.content})
            messages.append({"role": "user", "content": tool_results})
 
        return "Max iterations reached"
 
    def _handle_tool_calls(self, content, available_tools: list) -> list:
        results = []
        for block in content:
            if block.type != "tool_use":
                continue
 
            if block.name == "tool_search":
                found = self._search_tools(block.input["query"], available_tools)
                result = f"Loaded: {[t['name'] for t in found]}"
            else:
                result = self._execute(block.name, block.input)
 
            results.append({
                "type": "tool_result",
                "tool_use_id": block.id,
                "content": json.dumps(result)
            })
        return results
 
    def _search_tools(self, query: str, available_tools: list) -> list:
        found = []
        for name, tool in self.tool_catalog.items():
            if query.lower() in tool["description"].lower() and name not in self.loaded_tools:
                self.loaded_tools[name] = tool
                available_tools.append(tool)
                found.append(tool)
        return found
 
    def _execute(self, name: str, inputs: dict) -> Any:
        if name in self.tool_catalog:
            return {"result": f"Executed {name}", "inputs": inputs}
        return {"error": f"Tool '{name}' not found"}
 
    def _tool_search_definition(self) -> dict:
        return {
            "name": "tool_search",
            "description": "Search for available tools in the catalog",
            "input_schema": {
                "type": "object",
                "properties": {"query": {"type": "string"}},
                "required": ["query"]
            }
        }
 
    def _extract_text(self, response) -> str:
        for block in response.content:
            if hasattr(block, "text"):
                return block.text
        return "Complete"

Semantic Tool Search for Large Catalogs

For production with 100+ tools, upgrade from keyword matching to semantic search:

# pip install sentence-transformers faiss-cpu
 
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
 
class SemanticToolSearch:
    def __init__(self, tool_catalog: dict):
        self.model = SentenceTransformer("paraphrase-multilingual-MiniLM-L12-v2")
        self.tools = list(tool_catalog.values())
 
        descriptions = [t["description"] for t in self.tools]
        embeddings = self.model.encode(descriptions)
 
        self.index = faiss.IndexFlatL2(embeddings.shape[1])
        self.index.add(embeddings.astype(np.float32))
 
    def search(self, query: str, k: int = 5) -> list[dict]:
        query_vec = self.model.encode([query]).astype(np.float32)
        distances, indices = self.index.search(query_vec, k)
        return [self.tools[i] for i in indices[0] if distances[0][list(indices[0]).index(i)] < 1.5]

Troubleshooting

Error: Missing tool_result after tool_use

# ❌ Wrong — adding a user message without tool_result
messages.append({"role": "assistant", "content": response.content})
# Direct user message here will cause an API error
 
# ✅ Correct — always return tool_result for every tool_use block
tool_results = [
    {"type": "tool_result", "tool_use_id": block.id, "content": "..."}
    for block in response.content if block.type == "tool_use"
]
messages.append({"role": "user", "content": tool_results})

Tool Search returns no results

# ❌ Query too specific
tool_search_handler("function that retrieves Tokyo weather in celsius")
 
# ✅ Use short, general keywords
tool_search_handler("weather")  # Short keywords work better

Context grows too large in multi-step workflows

# ✅ Truncate large tool results before returning them
def truncate_result(result: dict, max_chars: int = 500) -> str:
    result_str = json.dumps(result)
    if len(result_str) > max_chars:
        return result_str[:max_chars] + "...[truncated]"
    return result_str

Performance & Security

Cost Comparison

| Approach | Token Usage | Cost | |----------|------------|------| | Traditional (all tools upfront) | 100% | Baseline | | Tool Search + dynamic loading | 15% | −85% | | + Programmatic Tool Calling | 10% | −90% |

Security Best Practices

Validate all inputs: Use JSON Schema validation before executing any tool
Sandbox code execution: Use Claude's built-in code execution tool for untrusted operations
Cap iterations: Always set max_iterations (recommended: 10–20)
Audit logging: Log every tool call and result for compliance and debugging

The Core Loop — Getting the Fundamentals Right

Most tool use bugs come from misunderstanding the request-response cycle. Here's the correct flow:

Send user message + tool definitions to Claude
Claude returns a response with a tool_use block (stop_reason: "tool_use")
Your application runs the actual tool
Add the assistant message (Claude's previous response) AND the tool_result to messages
Send back to Claude — it generates the final answer (stop_reason: "end_turn")

Here's a complete, correct implementation:

import anthropic
import json
 
client = anthropic.Anthropic()
 
tools = [
    {
        "name": "get_weather",
        "description": (
            "Call this tool when the user asks about current weather conditions, "
            "temperature, or forecast for a specific city. Use it whenever the user "
            "mentions a location and wants weather information Claude doesn't already know."
        ),
        "input_schema": {
            "type": "object",
            "properties": {
                "city": {
                    "type": "string",
                    "description": "City name (e.g., Tokyo, London, New York)"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                    "description": "Temperature unit. Defaults to celsius if not specified."
                }
            },
            "required": ["city"]
        }
    }
]
 
messages = [{"role": "user", "content": "What's the weather like in Tokyo right now?"}]
 
# Step 1: Initial request
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    messages=messages
)
 
# Step 2: Check stop_reason
if response.stop_reason == "tool_use":
    # Extract the tool_use block
    tool_use_block = next(
        block for block in response.content
        if block.type == "tool_use"
    )
    tool_name = tool_use_block.name
    tool_input = tool_use_block.input
    tool_use_id = tool_use_block.id
    
    # Step 3: Execute the actual tool
    # (In a real app, this calls an external API or database)
    result = {"temperature": "18°C", "condition": "Sunny", "humidity": "55%"}
    
    # Step 4: Add BOTH the assistant message AND the tool_result
    messages.append({"role": "assistant", "content": response.content})  # required!
    messages.append({
        "role": "user",
        "content": [
            {
                "type": "tool_result",
                "tool_use_id": tool_use_id,   # must match the tool's id
                "content": json.dumps(result)
            }
        ]
    })
    
    # Step 5: Get the final answer
    final_response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=messages
    )
    
    print(final_response.content[0].text)

Error Pattern #1: Claude Never Calls the Tool

Symptom: You defined tools and sent a message, but Claude responds with plain text and ignores the tools entirely.

Root cause A: Vague or missing tool description

The description field is how Claude decides when to use a tool. If it's generic or missing, Claude may conclude the tool isn't relevant.

# Bad — Claude doesn't know when to use this
{
    "name": "search",
    "description": "Searches for things",
    ...
}
 
# Good — explicit trigger conditions
{
    "name": "search_product_catalog",
    "description": (
        "Call this tool when the user asks about a specific product, its price, "
        "availability, or technical specifications. Use it whenever the user mentions "
        "a product name or SKU that Claude doesn't have in its training data. "
        "Do NOT use for general questions about product categories or comparisons."
    ),
    ...
}

Root cause B: No tool_choice enforcement

If you want Claude to always use a tool regardless of the question, use tool_choice:

# Force a specific tool
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    tool_choice={"type": "tool", "name": "get_weather"},
    messages=messages
)
 
# Force any tool to be called
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    tools=tools,
    tool_choice={"type": "any"},
    messages=messages
)

Error Pattern #2: Incorrect tool_result Format

Symptom: You're returning results but getting 400 Bad Request errors or the conversation ends unexpectedly.

This is the single most common tool use bug. The tool_result format is strict.

# WRONG: tool_result sent as assistant role
messages.append({
    "role": "assistant",  # tool_results must be in user role!
    "content": [{"type": "tool_result", ...}]
})
 
# WRONG: Missing tool_use_id
messages.append({
    "role": "user",
    "content": [
        {
            "type": "tool_result",
            # "tool_use_id": tool_use_id  <- omitting this breaks the correlation
            "content": "result text"
        }
    ]
})
 
# WRONG: Forgot to add the assistant message first
# (Adding only tool_result without the preceding assistant message)
 
# CORRECT: Always add assistant message first, then tool_result in user role
messages.append({"role": "assistant", "content": response.content})  # add this first
messages.append({
    "role": "user",
    "content": [
        {
            "type": "tool_result",
            "tool_use_id": tool_use_block.id,   # required
            "content": json.dumps(result)
        }
    ]
})

Error Pattern #3: JSON Schema Definition Mistakes

Symptom: Tool calls succeed sometimes but fail others, or Claude passes unexpected argument types.

Missing required array

# Without required, Claude may treat all fields as optional
{
    "type": "object",
    "properties": {
        "city": {"type": "string"},
        "unit": {"type": "string"}
    }
    # Missing: "required": ["city"]
}
 
# Correct: explicitly declare required fields
{
    "type": "object",
    "properties": {
        "city": {
            "type": "string",
            "description": "City name"
        },
        "unit": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "Temperature unit. Defaults to celsius."
        }
    },
    "required": ["city"]
}

Defensive input validation

Claude sometimes returns strings for numeric fields, or null for fields it's uncertain about. Always validate:

def execute_tool_safely(tool_name: str, tool_input: dict) -> str:
    """Validate and execute a tool call defensively."""
    
    if tool_name == "calculate_total":
        quantity = tool_input.get("quantity")
        
        # Claude might pass a string like "5" instead of integer 5
        if isinstance(quantity, str):
            try:
                quantity = int(quantity)
            except ValueError:
                return json.dumps({"error": "quantity must be an integer"})
        
        if quantity is None or quantity < 0:
            return json.dumps({"error": "quantity must be a non-negative integer"})
        
        unit_price = float(tool_input.get("unit_price", 0))
        return json.dumps({"total": quantity * unit_price, "currency": "USD"})
    
    return json.dumps({"error": f"Unknown tool: {tool_name}"})

Error Pattern #4: Parallel Tool Calls

Symptom: Claude returned multiple tool_use blocks in one response, and you're not sure how to handle them — or you returned only some results and it broke.

When Claude returns multiple tool_use blocks, you must return a tool_result for every single one.

def process_parallel_tool_calls(response, messages, tools):
    """Handle multiple tool_use blocks from a single Claude response."""
    
    if response.stop_reason != "tool_use":
        return response
    
    tool_use_blocks = [
        block for block in response.content
        if block.type == "tool_use"
    ]
    
    # Execute all tools (optionally in parallel with asyncio or ThreadPoolExecutor)
    tool_results = []
    for tool_block in tool_use_blocks:
        result = execute_tool_safely(tool_block.name, tool_block.input)
        tool_results.append({
            "type": "tool_result",
            "tool_use_id": tool_block.id,  # match each tool's specific id
            "content": result
        })
    
    # Add assistant message, then ALL tool results together
    messages.append({"role": "assistant", "content": response.content})
    messages.append({
        "role": "user",
        "content": tool_results  # list of all tool results
    })
    
    return client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=2048,
        tools=tools,
        messages=messages
    )

Error Pattern #5: Propagating Tool Errors to Claude

Symptom: Your external API returned an error during tool execution. You need Claude to handle it gracefully.

Use the is_error flag to tell Claude a tool failed:

def handle_tool_execution(tool_use_block):
    """Execute a tool and return an appropriate tool_result."""
    
    try:
        result = call_external_api(tool_use_block.input)
        return {
            "type": "tool_result",
            "tool_use_id": tool_use_block.id,
            "content": json.dumps(result)
        }
    
    except ExternalAPIError as e:
        # Tell Claude this tool call failed
        return {
            "type": "tool_result",
            "tool_use_id": tool_use_block.id,
            "content": f"API request failed: {str(e)} (status: {e.status_code})",
            "is_error": True  # Claude will acknowledge the failure in its response
        }
    
    except Exception as e:
        return {
            "type": "tool_result",
            "tool_use_id": tool_use_block.id,
            "content": f"Unexpected error during tool execution: {str(e)}",
            "is_error": True
        }

When Claude receives is_error: true, it acknowledges the failure and adjusts its response — for example, offering an alternative or explaining what went wrong to the user.

Error Pattern #6: Streaming + Tool Use

Symptom: You enabled streaming but don't know how to extract tool_use blocks from the stream.

With streaming, you accumulate partial JSON deltas and reconstruct the tool_use block at the end:

def stream_with_tool_use(messages, tools):
    """Process streaming response with tool use."""
    
    input_json_accumulator = {}
    content_blocks = []
    
    with client.messages.stream(
        model="claude-sonnet-4-6",
        max_tokens=1024,
        tools=tools,
        messages=messages
    ) as stream:
        for event in stream:
            if event.type == "content_block_start":
                if event.content_block.type == "tool_use":
                    input_json_accumulator[event.index] = ""
                    content_blocks.append({
                        "type": "tool_use",
                        "id": event.content_block.id,
                        "name": event.content_block.name,
                        "input": {}
                    })
                elif event.content_block.type == "text":
                    content_blocks.append({"type": "text", "text": ""})
            
            elif event.type == "content_block_delta":
                if event.delta.type == "input_json_delta":
                    input_json_accumulator[event.index] += event.delta.partial_json
                elif event.delta.type == "text_delta":
                    content_blocks[event.index]["text"] += event.delta.text
            
            elif event.type == "content_block_stop":
                if event.index in input_json_accumulator:
                    try:
                        content_blocks[event.index]["input"] = json.loads(
                            input_json_accumulator[event.index]
                        )
                    except json.JSONDecodeError:
                        content_blocks[event.index]["input"] = {}
        
        final_message = stream.get_final_message()
        return final_message, content_blocks

Production Retry Design

In production, wrap tool use calls in retry logic for rate limits and server errors:

import time
from anthropic import APIStatusError, APIConnectionError
 
def robust_tool_call(messages, tools, max_retries=3):
    """Tool call with exponential backoff retry."""
    
    for attempt in range(max_retries):
        try:
            return client.messages.create(
                model="claude-sonnet-4-6",
                max_tokens=1024,
                tools=tools,
                messages=messages
            )
        
        except APIStatusError as e:
            if e.status_code == 429:
                wait = 2 ** attempt  # exponential backoff: 1, 2, 4 seconds
                print(f"Rate limit hit. Retrying in {wait}s... ({attempt + 1}/{max_retries})")
                time.sleep(wait)
            elif e.status_code in [500, 503]:
                wait = 5 * (attempt + 1)
                print(f"Server error. Retrying in {wait}s...")
                time.sleep(wait)
            else:
                raise  # Don't retry 4xx errors (bad request, auth issues, etc.)
        
        except APIConnectionError:
            time.sleep(3 * (attempt + 1))
    
    raise Exception(f"All {max_retries} retry attempts failed")

Looking back

The most common tool use errors and their fixes:

Tool never called → Improve description with explicit trigger conditions; use tool_choice to force it
tool_result format errors → Use user role; include tool_use_id; always add the preceding assistant message
Schema definition bugs → Declare required array; validate and coerce input types defensively
Parallel calls broken → Return a tool_result for every tool_use_id in the same response
Tool execution errors → Use is_error: true so Claude can respond appropriately
Streaming issues → Accumulate input_json_delta events and parse at content_block_stop

Once you have a working pattern, tool use becomes remarkably reliable. The key is treating the request-response cycle as a strict protocol rather than a loose convention.

For a broader introduction to the Claude API, see the Claude API First Call Guide.

Summary & Next Steps

In this guide you implemented all three advanced Claude API tool use features:

Tool Search Tool: Dynamic tool discovery with 85% token reduction — scales to any catalog size
Programmatic Tool Calling: In-code orchestration with 37% latency improvement — keeps context lean
Tool Use Examples: Usage patterns for 90% parameter accuracy — critical for complex tool interfaces

Combining all three enables enterprise-grade agent systems with hundreds to thousands of tools.

Continue your learning with these related guides:

[Streaming Responses with Tool Use]((/articles/api-sdk/claude-api-streaming-tool-use)
[Production Multi-Agent System Design]((/articles/api-sdk/claude-agent-sdk-production-multi-agent-system)
[Building an MCP Server — Externalizing Your Tool Catalog]((/articles/api-sdk/build-mcp-server)

Thank You for Reading

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