The New Claude “Think Tool” From Anthropic For Improved Agentic Tool Use
Improved AI Reasoning Through Structured Thinking
Is it a Tool?
Does the “Anthropic Claude Think Tool” qualify as a tool in the sense of AI Agent architecture?
Based on Anthropic’s documentation, the Claude Think Tool is not a standalone tool or a traditional tool component of the AI Agent architecture.
Instead, it refers to Claude’s extended thinking mode or think step-by-step capability, a feature that enhances Claude’s reasoning process for complex tasks.
This mode allows Claude to break down problems systematically, generating intermediate reasoning steps before providing a final answer.
It’s implemented as part of Claude’s API, where developers can enable this mode with specific parameters, such as allocating additional tokens for reasoning.
It’s a built-in feature of Claude’s API, not an external tool that Claude calls. Developers control it via API parameters, such as setting a higher token budget for thinking steps or enabling verbose reasoning outputs.
And it is designed to improve Claude’s performance on complex tasks by mimicking human-like deliberation, making it valuable for developers building applications where explainability or precision is critical.
AI Agent Architecture and Tools
Before we move on to the Claude Think Tool, just a word on AI Agent Tools…
In AI Agent architecture, a tool typically refers to an external resource or function that an AI Agent can invoke to augment its capabilities.
This is common in frameworks like LangChain or LlamaIndex, where AI Agents use tools such as:
- External APIs (for example, a weather API for real-time data).
- Databases (for example, querying a CRM for customer data).
- Specialised functions (for example, a calculator for precise math or a code interpreter for running scripts).
- Search engines (for example, web search for real-time information).
Is the “Claude Think Tool” an AI Agent tool in This Sense?
No, the Claude Think Tool is not a tool in the AI agent architecture sense.
It Is A Feature or Mode
Instead, the Claude Think Tool is better described as a feature or mode of Claude’s API, designed to improve performance on tasks requiring deep reasoning.
It’s analogous to prompting techniques like “chain-of-thought” (CoT) reasoning, but formalised within Claude’s API for developer control.
Relevance to End-User UI vs. Builder API
We have come to know detailed reasoning chain of thought through the DeepSeek Chat web UI.
But there is a difference between a horizontal, end-user focussed UI like DeepSeek Chat, a the Claude Think Tool which is focussed on builders creating vertical focussed specific solutions.
Just to break this distinction down further…
End-User UI (DeepSeek Chat)
DeepSeek Chat is a fully built interface where reasoning capabilities (like those of DeepSeek-V3) are embedded in a user-friendly conversational flow.
It doesn’t expose features like Claude’s Think Tool explicitly to users.
Instead, any step-by-step reasoning is handled internally and presented in a simplified way.
Users don’t need to configure reasoning modes or manage token budgets — they interact with a polished UI designed for general tasks.
DeepSeek Chat doesn’t emphasise explainable reasoning as a distinct feature. While it can break down problems, this is part of its standard output, not a developer-controlled mode like Claude’s Think Tool.
In the context of AI Agent architecture, DeepSeek Chat doesn’t natively expose tool-calling capabilities to end users, though DeepSeek’s API might support such integrations for developers.
Builder API (Claude API with Think Tool)
Claude’s API, including the Think Tool, is designed for developers to build custom applications.
The Think Tool is a developer-facing feature, allowing fine-grained control over Claude’s reasoning process.
Unlike DeepSeek Chat, the Claude API doesn’t provide a ready-made UI.
Developers must create their own interface or integrate Claude into existing systems, making it suitable for industry-specific solutions.
What is Claude’s Think Tool?
At its core, the Think Tool is a structured approach that allows Claude to think before speaking — similar to how humans pause to work through challenging problems before providing an answer.
Rather than rushing to generate a final response immediately, the model is prompted to explicitly work through its reasoning process step by step.
The Think Tool integrates two key components:
- A structured thinking format where Claude breaks down problems and reasons through them systematically
- A training approach that teaches the model to use this structured thinking process effectively
This approach helps Claude tackle problems that require careful reasoning, such as complex math problems, logical reasoning, and multi-step analytical tasks.
Below, the code illustrates a two-step process for leveraging Claude’s reasoning capabilities via Anthropic’s API.
In the first API call, Claude is prompted to generate intermediate thinking steps, such as step-by-step reasoning.
In the second API call, Claude uses a separate prompt (prompt_for_answer) to produce the final answer, building on the reasoning from the first call.
# First API call - Claude generates the thinking steps
response = self._call_claude_api(prompt_for_thinking)
# Second API call - Claude generates the final answer
response = self._call_claude_api(prompt_for_answer)Why the Think Tool Matters
- Improved accuracy: By breaking down complex problems, Claude can avoid common reasoning errors that occur when trying to solve everything at once
- Transparency: The explicit reasoning steps make it easier for users to follow Claude’s logic and identify any mistakes
- Educational value: Users can learn from seeing how Claude approaches different types of problems
- Adaptability: The structured format works across a wide range of reasoning tasks.
A Practical Example in Python
The code below you can copy and paste into a Python Notebook…
You will just need to enter your Anthropic API key…
!pip install requests ipythonimport time
import os
import requests
import json
import getpass
from IPython.display import display, Markdownclass ThinkTool:
def __init__(self, api_key=None, model="claude-3-opus-20240229", show_thinking=True):
# Prompt for API key if not provided
if api_key is None:
api_key = getpass.getpass("Enter your Anthropic API key: ")
self.api_key = api_key
self.model = model
self.show_thinking = show_thinking
self.api_url = "https://api.anthropic.com/v1/messages"
def solve(self, problem):
display(Markdown(f"**Problem:** {problem}\n"))
# Generate thinking using Claude API
thinking = self._generate_thinking(problem)
# Show thinking if enabled
if self.show_thinking and thinking:
display(Markdown("### Thinking process:"))
for step in thinking:
display(Markdown(f"- {step}"))
time.sleep(0.3) # Small delay for readability
# Generate final answer based on thinking
answer = self._generate_answer(problem, thinking)
display(Markdown(f"### Final answer:\n{answer}"))
return answer
def _generate_thinking(self, problem):
"""Generate structured thinking steps using Claude API"""
try:
prompt = f"""
I need to solve this problem: {problem}
Please help me think through this step by step. Only provide the individual thinking steps, each on a new line starting with "Step: ".
Don't provide a final answer yet, just the reasoning process.
"""
response = self._call_claude_api(prompt)
# Extract thinking steps from response
thinking_text = response.get('content', [{}])[0].get('text', '')
thinking_steps = [step.replace("Step: ", "").strip()
for step in thinking_text.split('\n')
if step.strip().startswith("Step:")]
# If no steps found with "Step:" prefix, split by lines and filter empty lines
if not thinking_steps:
thinking_steps = [line.strip() for line in thinking_text.split('\n') if line.strip()]
return thinking_steps
except Exception as e:
display(Markdown(f"**Error generating thinking:** {str(e)}"))
return []
def _generate_answer(self, problem, thinking=None):
"""Generate final answer based on problem and optionally considering thinking steps"""
try:
if thinking:
thinking_text = "\n".join([f"- {step}" for step in thinking])
prompt = f"""
Problem: {problem}
I've thought through this problem with these steps:
{thinking_text}
Based on the above thinking, what is the final answer?
Provide a clear, concise conclusion without repeating all the steps.
"""
else:
prompt = f"""
Problem: {problem}
What is the final answer to this problem? Provide a clear, concise solution.
"""
response = self._call_claude_api(prompt)
answer_text = response.get('content', [{}])[0].get('text', 'No answer generated')
return answer_text
except Exception as e:
display(Markdown(f"**Error generating answer:** {str(e)}"))
return "Failed to generate an answer due to an error."
def _call_claude_api(self, prompt):
"""Call the Anthropic Claude API with the given prompt"""
headers = {
"x-api-key": self.api_key,
"anthropic-version": "2023-06-01",
"content-type": "application/json"
}
data = {
"model": self.model,
"max_tokens": 1000,
"messages": [{"role": "user", "content": prompt}]
}
response = requests.post(self.api_url, headers=headers, json=data)
if response.status_code != 200:
raise Exception(f"API request failed with status code {response.status_code}: {response.text}")
return response.json()def demo_think_tool():
# Available Claude models
available_models = [
"claude-3-opus-20240229",
"claude-3-sonnet-20240229",
"claude-3-haiku-20240307"
]
print("Available Claude models:")
for i, model in enumerate(available_models, 1):
print(f"{i}. {model}")
try:
model_choice = int(input("Select a model (1-3) [default: 1]: ") or "1")
if 1 <= model_choice <= len(available_models):
selected_model = available_models[model_choice-1]
else:
print(f"Invalid choice. Using {available_models[0]}")
selected_model = available_models[0]
except ValueError:
print(f"Invalid input. Using {available_models[0]}")
selected_model = available_models[0]
# Create ThinkTool instance (will prompt for API key)
claude = ThinkTool(model=selected_model, show_thinking=True)
# Example problems to demonstrate with
problems = [
"Solve the quadratic equation 2x² - 5x + 3 = 0.",
"If a train travels 120 miles in 2 hours, and another train travels 180 miles in 3 hours, which train is moving faster?",
"What's the next number in the sequence: 2, 6, 12, 20, 30, ...?"
]
# Let user choose which problem to solve
display(Markdown("### Choose a problem to solve:"))
for i, problem in enumerate(problems, 1):
print(f"{i}. {problem}")
user_choice = input("Enter a problem number (1-3) or type your own problem: ")
try:
problem_idx = int(user_choice) - 1
if 0 <= problem_idx < len(problems):
problem = problems[problem_idx]
else:
problem = user_choice
except ValueError:
problem = user_choice
# Solve the selected problem
# Moved claude.solve(problem) inside the function
claude.solve(problem)
# Call the function to execute the code
demo_think_tool() # Added this line to call the functionAnd below the output from the Notebook…
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
