Give Claude more room to reason through a problem

The problem

A quick answer can miss dependencies or trade-offs in a problem that needs several steps. Your agent needs a way to request more reasoning effort before it turns that answer into a plan.

How NativePort helps

NativePort connects your agent to Claude through Anthropic and lets it request more effort for a difficult question. Your agent can use the resulting analysis to compare options and present a plan you can review.

For example, it can ask Claude to arrange a small project around dependencies and limited time. More reasoning can help, but the agent still checks whether the proposed schedule respects your constraints.

Let your agent build it

You don’t need to write this code yourself. Copy the prompt and paste it into your agent. It will follow NativePort’s setup guide, help you use an existing account or create one, read this page, and ask what you’d like to do next.

View prompt

Help me use NativePort for this use case.

1. Read https://nativeport.ai/SKILL.md and follow its instructions to learn how to use NativePort. Save or update the skill if your environment supports it.

2. Check whether I already have a working NativePort connection or account. Reuse it if available. Otherwise, help me sign in or create an account by following the skill's setup instructions. Guide me through any steps that require me to use a browser, then help connect my agent. Keep credentials private.

3. Follow the skill to learn how to find the right tools, check their prices and instructions, and use them through NativePort.

4. Read this entire use-case page, including the instructions for agents inside the accordion: https://nativeport.ai/use-cases/use-claude-extended-thinking/

Once setup is complete and you have read the page, briefly explain what you can help me do and ask me what I would like to do next. Wait for my answer before implementing the example or running paid tools.

Instructions for the agents - You don't need to read this

Technical implementation

Use Python 3. Install requests with python -m pip install requests and set NATIVEPORT_API_KEY to your NativePort key in your server environment. Keep the key out of browser code.

Set CLAUDE_MODEL to a native model ID listed by GET /anthropic/v1/models that supports adaptive thinking and output_config.effort. This example uses the current adaptive configuration. Older models may require manual budget_tokens; do not send this configuration to them without checking compatibility.

python

import json
import os
import requests

response = requests.post("https://api.nativeport.ai/anthropic/v1/messages",
    headers={"Authorization": f"Bearer {os.environ['NATIVEPORT_API_KEY']}",
             "anthropic-version": "2023-06-01"},
    json={"model": os.environ["CLAUDE_MODEL"], "max_tokens": 4000,
          "thinking": {"type": "adaptive"}, "output_config": {"effort": "high"},
          "messages": [{"role": "user", "content":
              "Plan a two-person project. Research takes 2 person-days, design takes 2 "
              "and must follow research, implementation takes 4 and must follow design, "
              "and testing takes 2 and must follow implementation. Each person has one "
              "person-day available per weekday. Tasks can be shared by both people. "
              "Return a feasible schedule, its assumptions, and a short constraint check."}]}, timeout=180)
response.raise_for_status()
result = response.json()
if result.get("stop_reason") != "end_turn":
    raise RuntimeError(f"Incomplete answer: {result.get('stop_reason')}")
answer = "\n".join(block["text"] for block in result.get("content", [])
                   if block.get("type") == "text")
if not answer:
    raise RuntimeError("No final answer returned")
print(answer)
print(json.dumps({"usage": result.get("usage", {})}, indent=2))

Adaptive thinking allows Claude to decide how much reasoning to use; high effort is not a guaranteed fixed thinking budget. The snippet displays only the final answer, not thinking blocks. Validate dependencies and resource limits before using the schedule. Do not automatically retry a rejected configuration with a different paid model.

Tool costs

Tool used in the example	Price per call
Anthropic — one Messages request with adaptive thinking	Varies by selected model and token usage

The example makes one model request with a maximum output allowance of 4,000 tokens. The price depends on the selected model and actual input and output, including billable thinking. The visible final answer alone does not represent all output usage.

Prices in USD. Usage-based tools have no fixed per-call price. View pricing.

The problem

How NativePort helps

Let your agent build it

Technical implementation

Tool costs

Keep exploring

Check what a model can do before choosing it

Get AI answers in a consistent structured format

Reuse reference material across Claude requests

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