Teaching AI to Use Tools — The Right Way

A Deep Dive into Seal-Tools: The Dataset That Makes LLMs Smarter Agents

Imagine asking your AI assistant to “book a flight to Paris, then schedule a taxi to the airport and convert the final bill to Euros.” Sounds simple, right? In reality, for most AI models, this isn’t just hard — it’s nearly impossible to get right without human babysitting.

That’s because tool use, chaining functions, and executing multi-step operations requires structured reasoning, parameter handling, and format control — things even the smartest LLMs struggle with today.

This is the exact problem that the new research paper Seal-Tools: Self-Instruct Tool Learning Dataset for Agent Tuning and Detailed Benchmark seeks to solve. If you’re interested in building reliable AI agents, this paper is a must-read — and this post will walk you through why.

Why Tool Use Is the Future of AI

Large Language Models like GPT-4 and Claude have sparked a revolution in natural language understanding. But the next frontier is not just understanding — it’s action.

Tools are the hands of the AI. Without them, a model can speak, but it cannot do.

An AI that writes a travel plan is helpful. But one that calls an API to book the trip, checks the weather, converts currencies, and sends you a summary — now that’s a true agent.

This kind of reasoning requires a model to:

• Choose the right tool for the job
• Fill in the correct parameters
• Follow strict output formats (e.g., JSON or function calls)
• Chain tools across multiple steps

Unfortunately, even the most powerful models today fall short. That’s where Seal-Tools steps in.

What Is Seal-Tools?

Seal-Tools is a large-scale, structured dataset built to train and evaluate LLMs in tool-use scenarios. The core idea is to teach AI how to call tools just like developers use APIs — only in natural language.

It includes:

• 1,042 unique tools across diverse domains
• Over 100,000 tool-use instances generated using Self-Instruct
• Examples of multi-tool, nested, and chained operations
• Strict JSON-format outputs for consistency and automation

Each tool is described just like a real-world API:

{
  "name": "currency_converter",
  "description": "Convert a value between currencies.",
  "parameters": {
    "from_currency": "USD",
    "to_currency": "EUR",
    "amount": "100.0"
  },
  "required": ["from_currency", "to_currency", "amount"]
}

And each task instance simulates a realistic scenario:

“Convert 100 USD to EUR, and then use the result to calculate VAT in France.”

How It Was Built — The Self-Instruct Pipeline

What’s genius about Seal-Tools is that it uses LLMs to generate the dataset themselves — but with checks, balances, and curation.

Here’s the step-by-step pipeline:

• Field Generation – Domains like finance, health, or travel are first generated.
• Tool Design – Each field includes several tool descriptions.
• Instance Generation – Each tool gets basic and complex tool-usage tasks.
• Validation – JSON output is validated for structure and semantic consistency.

This lets researchers generate massive datasets quickly while maintaining realism and structure.

Evaluation: Metrics That Actually Matter

Seal-Tools introduces three targeted evaluation metrics:

1. Format Accuracy – Can the model generate valid JSON or function calls?
2. Tool Selection Accuracy – Did it choose the correct tools for the task?
3. Parameter Filling Accuracy – Did it supply correct and complete values?

These are objective and automatable, unlike vague scores used in standard NLP evaluations.

What Did the Experiments Reveal?

Models tested in the paper include:

• GPT-3.5 and GPT-4
• Claude 2
• LLaMA2-7B, Vicuna, Mistral-7B
• A fine-tuned LLaMA2 trained on Seal-Tools

Key takeaways:

• GPT-4 leads the pack, but still struggles with nested or chained tool usage.
• Open-source models have significant limitations out-of-the-box.
• Fine-tuning on Seal-Tools improves tool performance by 15–20%.

Even top models are not great tool users… yet.

How Seal-Tools Stands Out

Seal-Tools is bigger, more complex, and better structured than any other tool-using dataset available.

Why This Matters for Agentic AI

We’re entering an era of agentic computing, where LLMs are expected to plan, decide, and act on our behalf.

For this to work, models must:

• Understand tool specifications
• Format structured calls
• Handle edge cases and data dependencies
• Chain multiple tools together intelligently

Seal-Tools is the training ground for this future. It’s more than a dataset — it’s a curriculum for teaching LLMs real-world behavior.

Links & Resources

• Read the paper: arXiv:2405.08355
• Explore the GitHub repo: github.com/fairyshine/Seal-Tools
• View evaluation & fine-tuning: evaluation scripts

Final Thoughts

Seal-Tools offers a major step forward in developing LLMs that go beyond chatting and start executing real tasks. It’s built on the idea that agents must not just talk, but do — and gives us the tools to train them accordingly.

Whether you’re building autonomous agents, developing smart assistants, or researching LLM capabilities, Seal-Tools should be part of your stack.

With this dataset, we’re not just teaching AI how to use tools —
we’re teaching it how to think in actions.