Tools
Tools enable AI systems to interact with external services and perform actions. They provide a structured way for agents and workflows to invoke APIs, manipulate data, and integrate with external systems. Tools form the bridge between AI decision-making capabilities and real-world actions.
Cloudflare Agents support several tool patterns. Choose the smallest one that fits the job:
| Pattern | Use when | Start here |
|---|---|---|
| Server-side tools | The tool can run entirely in the Worker, such as fetching an API or querying SQL | Chat agents |
| Client-side tools | The tool needs browser APIs such as geolocation, clipboard, or local storage | Chat agents |
| Human approvals | The tool is sensitive and needs a user decision before it runs | Human-in-the-loop |
| MCP tools | You want to expose or consume tools through the Model Context Protocol | Model Context Protocol |
| Agent tools | You want a chat agent to run another chat-capable sub-agent as a retained, streaming tool | Agent tools |
In an AI system, tools are typically implemented as function calls that the AI can use to accomplish specific tasks. For example, a travel booking agent might have tools for:
- Searching flight availability
- Checking hotel rates
- Processing payments
- Sending confirmation emails
Each tool has a defined interface specifying its inputs, outputs, and expected behavior. This allows the AI system to understand when and how to use each tool appropriately.
The most common type of tools are those that wrap external APIs. These tools handle the complexity of API authentication, request formatting, and response parsing, presenting a clean interface to the AI system.
The Model Context Protocol ↗ provides a standardized way to define and interact with tools. Think of it as an abstraction on top of APIs designed for LLMs to interact with external resources. MCP defines a consistent interface for:
- Tool Discovery: Systems can dynamically discover available tools
- Parameter Validation: Tools specify their input requirements using JSON Schema
- Error Handling: Standardized error reporting and recovery
- State Management: Tools can maintain state across invocations
Tools that handle data transformation and analysis are essential for many AI workflows. These might include:
- CSV parsing and analysis
- Image processing
- Text extraction
- Data validation