Hey @snwmbpro-4756!

The number of tools you can realistically define depends on a few things: your model’s context window, how complex each tool is, and overall performance.

Here’s a simple way to think about it:

Context Window Considerations

• Every tool definition takes up space in your context window
• The more detailed or complex the schema, the more tokens it eats
• Models like Claude 3.5 Sonnet (with a 200K context) give you more room to play with, but you still want to be intentional

What’s Practical

• 10–15 tools: This is the sweet spot for most setups
• 20–30 tools: Still doable, but you’ll want to keep an eye on performance
• 50+ tools: Generally not ideal — you’ll start seeing:
  • Slower responses
  • Higher token usage
  • Lower accuracy when picking between tools
  • Pressure on the context window

Best Practices

• Keep tool descriptions as clear and concise as possible
• Don’t over-engineer schemas — simple is better
• If multiple tools are tightly related, consider combining them
• Load tools dynamically when you can
• Keep an eye on token usage and the model’s output quality over time

When to Start Modularizing

When you hit 10–15 tools, it’s a good time to think about structure:

• Group tools by domain or purpose
• Load tools only when the model needs them
• Use routing or intent detection to direct which tool gets used

The AI SDK already gives you helpful utilities for managing all this, so you can tune things based on your actual token usage and workflow.

Let us know how you get on!

Hey @snwmbpro-4756! Just wanted to check in and see if you're still looking for insights on defining tool limits. Have you found a solution, or do you still need some help? Excited to help!