Tool use, also called function calling, is the mechanism that transforms a language model into an actual AI agent capable of taking real-world actions. Without it, AI systems can only generate text. With it, they can search databases, send emails, execute code, and interact with any system that exposes an application programming interface (API). This fundamental capability is reshaping how developers build AI agents in 2026, moving beyond chatbots that simply talk at users to agents that accomplish tasks .

What Separates an AI Agent from a Chatbot?

The distinction comes down to action. When a language model (LLM), a type of artificial intelligence trained on vast amounts of text, receives a request, it can either generate a text response or invoke an external function based on the conversation context. Tool use enables the second path. Instead of saying "I would search for that information," the model outputs a structured call to a specific tool with specific arguments. The application executes the tool and returns the result, allowing the model to continue reasoning and take the next step .

This capability unlocks practical workflows. An agent might search for a customer by email, retrieve their order history, find a specific order, check return eligibility, and initiate the return process, all in sequence. Each step depends on the previous one, creating a chain of actions that solves real problems .

How Do Different AI Platforms Implement Tool Use?

The three major AI providers, OpenAI, Anthropic, and Google, each implement function calling with slightly different approaches, though the core concept remains the same. Understanding these differences matters for developers choosing which platform to build on.

OpenAI's implementation uses a tools parameter in the chat completions API. Developers define tools as JSON Schema objects, which is a standardized format for describing data structures. The key fields include the function name, a description explaining what it does and when to use it, and parameters defining the expected arguments. OpenAI also supports tool_choice, which can force the model to call a specific tool or let it decide automatically. In production, developers typically force tool calling at the start of workflows where the first step is known, then switch to automatic mode for the reasoning loop .

Anthropic's Claude takes a different approach. Tool descriptions are processed through Claude's instruction-following training, meaning longer, more detailed descriptions often produce better results than brief ones. Anthropic explicitly recommends including example inputs and outputs in tool descriptions. When Claude decides to call a tool, the response contains a tool_use content block with the tool name and JSON input. Developers execute the tool and send the result back as a tool_result content block in the next message. Claude also supports calling multiple tools in parallel within a single response, which is particularly useful for agents that need to gather data from multiple sources simultaneously .

Google's Gemini defines tool use through function_declarations in the generation config. The JSON Schema for parameters follows the same standard, but Gemini adds a behavior configuration that controls how aggressively the model uses tools. Developers can set it to AUTO (model decides), ANY (model must call at least one tool), or NONE (tool calling disabled). Gemini also supports code_execution as a built-in tool, allowing the model to write and run Python code in a sandboxed environment, which is uniquely powerful for mathematical and data transformation tasks .

What Makes Tool Descriptions Actually Work?

The quality of tool descriptions is the single biggest factor in whether an agent uses tools correctly. Vague or incomplete descriptions lead to hallucination, where the model invents tool names or generates malformed parameters. Effective descriptions follow specific patterns that work consistently across all providers .

• Explicit Usage Guidance: State clearly when to use the tool and when not to. For example, "Use this tool when the user asks about current prices, inventory levels, or product availability. Do NOT use this tool for historical data, use the analytics_query tool instead."
• Parameter Constraints: Include constraints in the description itself. "The date parameter must be in YYYY-MM-DD format. The category must be one of: electronics, clothing, food."
• Concrete Examples: Provide sample inputs and outputs. "Example: search_database({query: 'red shoes', max_results: 5, category: 'clothing'})"
• Return Format Description: Explain what the tool returns. "Returns a JSON array of product objects with fields: id, name, price, in_stock."
• Error Conditions: State what happens when things go wrong. "Returns an error message if the query is empty or the category is not recognized."

How Should Developers Handle Tool Failures?

Tool execution failures are inevitable in production systems. Network timeouts, rate limits, and unexpected data formats all occur regularly. The way developers handle these failures determines whether agents recover gracefully or spiral into errors .

For transient failures like network timeouts or rate limits, implement retries with exponential backoff, which means waiting progressively longer between attempts. However, do not retry tool calls where the parameters were wrong. Instead, send the error back to the model so it can correct its approach. For critical workflows, define fallback tools that provide degraded but functional alternatives. If the primary database search fails, a cached or simplified search can keep the agent moving forward .

The error message sent back to the model matters enormously. Instead of a generic "Error occurred," send structured feedback: "The search_database tool returned an error: invalid date format '2026/03/30'. Expected format is YYYY-MM-DD. Please retry with the correct format." This gives the model the information it needs to self-correct .

Steps to Secure Tool Calling in Production

Never execute tool calls without validation. The model can hallucinate tool names, generate malformed parameters, or request actions outside its authorized scope. Developers must implement multiple layers of protection before any tool executes .

• Schema Validation: Validate every tool call against its JSON Schema before execution. Libraries like Pydantic for Python or Zod for TypeScript make this straightforward and reduce the risk of unexpected data types reaching downstream systems.
• Authorization Checks: Verify that the tool call is permitted given the current user's permissions. An agent serving customer A should not be able to query customer B's data, even if the model requests it.
• Rate Limiting: Prevent agents from making excessive tool calls. A bug or adversarial input could cause an infinite tool-calling loop that racks up costs or overwhelms downstream services.
• Output Sanitization: Tool results should be sanitized before being sent back to the model, especially if they contain user-generated content that could constitute a prompt injection attack.

Why Parallel Tool Calling Matters for Performance

Both OpenAI and Anthropic support parallel tool calling, meaning the model can request multiple tool calls in a single response. This is essential for performance. If an agent needs to check inventory and look up pricing simultaneously, parallel calls cut latency in half compared to sequential calls .

To enable effective parallel tool calling, design tools to be independent. Each tool should accept all the parameters it needs without depending on the output of another tool. When tools have dependencies, where tool B needs the output of tool A, the model will naturally sequence them across multiple turns .

In streaming mode, where responses arrive incrementally, tool calls arrive as partial JSON that must be accumulated until complete. This is a common source of bugs. Use the provider's streaming helpers rather than parsing the raw stream yourself. The edge cases around partial JSON, multiple tool calls in a single chunk, and interleaved text and tool content are tricky to handle correctly .

What Does the Future of Agent Development Look Like?

Tool use and function calling are foundational skills for any AI agent engineer. The ability to design clear tool descriptions, handle failures gracefully, validate inputs, and chain tool calls together separates production-ready agents from experimental prototypes. As more organizations move beyond chatbots to agents that actually accomplish tasks, these capabilities will become table stakes for AI development teams .

For developers building agents in 2026, the practical patterns are now well-established across major platforms. The challenge is no longer whether tool use is possible, but how to implement it reliably, securely, and at scale.