Tool Use in AI Agents: How Function Calling Works

Language models are remarkably capable at processing and generating text, but they cannot check the weather, query a database, send an email, or browse the web on their own. Tool use, implemented through function calling, bridges this gap by allowing AI agents to interact with external systems. When an agent "uses a tool," it generates a structured request that the host application executes and returns the result. This mechanism is what transforms a passive text generator into an active agent that can affect the real world.

How Function Calling Works

The function calling process follows a precise protocol between the application, the model, and external services:

1. Tool definition: The application provides the model with descriptions of available tools, including their names, purposes, and parameter schemas
2. Model decision: Based on the user's request and available tools, the model decides which tool (if any) to call and generates structured parameters
3. Application execution: The application receives the tool call, validates the parameters, executes the actual function, and captures the result
4. Result integration: The application sends the result back to the model, which uses it to formulate its response or decide on the next tool call

Crucially, the model never actually executes tools. It only generates the specification of what should be called with what parameters. The application is always the intermediary, which provides a natural point for validation, authorization, and logging.

Function calling is fundamentally a structured output mechanism. The model generates JSON conforming to a schema rather than free-form text. This structure is what makes tool use reliable and programmatically actionable.

Designing Effective Tool Definitions

The quality of your tool definitions directly affects how well the agent uses them. A good tool definition includes:

Clear, Descriptive Names

Tool names should be self-explanatory. search_customer_orders is better than query or search. The model uses the name as a primary signal for when to invoke the tool.

Detailed Descriptions

The description should explain what the tool does, when to use it, and what information it returns. Include examples of appropriate use cases and edge cases. The model relies heavily on descriptions to decide which tool to call, so vague descriptions lead to incorrect tool selection.

Well-Defined Parameter Schemas

Each parameter should have a clear type, description, and constraints. Required versus optional parameters should be explicitly specified. Enums should be used for parameters with a fixed set of valid values. The more precise the schema, the less likely the model is to generate invalid parameters.

Parallel and Sequential Tool Calls

Modern models support parallel tool calling, where the model requests multiple tool invocations in a single response. For example, when asked to compare weather in two cities, the model can call the weather API for both cities simultaneously rather than sequentially. This reduces latency and the number of round trips.

Sequential tool calling happens when one tool's output is needed as input for another. The model calls the first tool, receives the result, and then decides on the next tool call. This naturally supports multi-step workflows where each step depends on the previous result.

Error Handling and Recovery

Robust tool use requires comprehensive error handling. Tools will fail due to network issues, invalid parameters, rate limiting, permission errors, and countless other reasons. How you handle these errors determines whether the agent recovers gracefully or breaks down.

• Return informative error messages: When a tool fails, return a clear error message that helps the model understand what went wrong and how to fix it
• Include retry guidance: If the error is transient, indicate that the model should retry. If the parameters were wrong, explain what was invalid.
• Set timeout limits: Tools should have reasonable timeouts to prevent the agent from waiting indefinitely
• Provide fallback options: When possible, suggest alternative tools or approaches that might succeed

Validation Before Execution

Always validate tool parameters before executing the actual function. Check that required parameters are present and have valid values, that the requested operation is authorized for the current user, and that rate limits have not been exceeded. Catching errors before execution is faster and safer than handling failures after the fact.

Security Considerations

Tool use introduces security concerns that do not exist in text-only interactions. When an agent can call APIs, query databases, or execute code, the potential for harm increases significantly.

Authorization: Every tool call should be authorized in the context of the current user's permissions. Just because the agent wants to access a database does not mean the user has permission to see that data.

Input sanitization: Tool parameters should be treated as untrusted input. SQL injection, command injection, and path traversal attacks are possible if parameters are passed directly to backend systems without validation.

Rate limiting: Agents in loops can generate many tool calls quickly. Rate limiting prevents runaway costs and protects downstream services from overload.

Every tool call is a security boundary crossing. Treat tool parameters with the same caution you would treat user input in a web application: validate, sanitize, authorize, and log everything.

The Model Context Protocol (MCP)

The Model Context Protocol, introduced by Anthropic, standardizes how AI applications provide tools and context to models. MCP defines a client-server architecture where MCP servers expose tools, and MCP clients (AI applications) connect to them. This standardization allows tools to be developed independently and shared across different applications and models.

MCP is significant because it decouples tool implementation from agent implementation. A Slack integration MCP server can be used by any MCP-compatible agent, reducing duplication and fostering a shared ecosystem of tools.

Best Practices for Tool Design

1. Keep tools focused: Each tool should do one thing well. A tool that searches, filters, and formats results should be three separate tools.
2. Return structured data: JSON responses are easier for models to parse than free-form text
3. Include metadata: Return not just the result but information about result count, pagination, and confidence
4. Limit response size: Large tool responses consume context window space. Summarize or paginate when necessary.
5. Version your tools: As tools evolve, maintain backward compatibility or version them explicitly

As tool use standards like MCP mature and models become better at function calling, the ecosystem of available tools will grow rapidly. Designing tools that are clear, secure, and composable positions your agent systems to benefit from this expanding capability landscape.