Good AI agent tools have clear, distinct purposes, constrained inputs, useful results and safe application-side execution. Treat each tool call as a request—not an instruction that bypasses your application’s validation, permissions or approval policies—and test the interface against realistic tasks.
How an AI agent tool call works
A tool call is a structured request from a model. The model can choose a tool and provide arguments, but it does not execute your client-side code. Your application, or a provider-hosted tool platform, runs the operation and returns its result. The model may then answer the user or make another tool call.
OpenAI describes function calling as a request-and-response sequence: the application sends available tools, the model returns a call, application code executes it, and the application sends the output back. Anthropic describes the same basic contract for client-executed tools: the application receives a structured tool-use block, runs the operation and sends back a tool result. Some tools instead execute on the provider’s servers, which changes who operates the tool. See OpenAI’s function-calling documentation and Anthropic’s explanation of tool use.
That boundary is a security principle as well as an implementation detail. A model-generated request does not prove that the user is authorized to perform the action or that the arguments are safe. Validate the request and apply your own policies before execution.
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Design each tool around a clear job
Give a tool a specific purpose, a name that signals it and a description explaining when it is appropriate. The model should be able to distinguish it from neighboring tools and understand what task it enables. OpenAI’s Agents SDK recommends small, composable tools with one responsibility; Anthropic cautions against simply wrapping every existing endpoint without considering how an agent will interpret and choose actions. Anthropic develops this point in “Writing effective tools for AI agents,” published September 11, 2025.
Consider a weather assistant asked, “Should I bring an umbrella today?” A weather tool may be appropriate, but the agent may need the user’s location first—or may be able to answer a different part of the question from general knowledge. A useful description makes clear what location or other context the tool requires, rather than inviting a call with missing arguments.
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Tools should also support useful task subdivision without flooding the model with intermediate data. A focused operation that returns only the information needed for the next decision can be easier to use than a broad endpoint that produces a large, noisy response.
How many tools should an AI agent have?
There is no universal ideal count established by the reviewed provider guidance. More tools are not inherently better: similar tools with vague or overlapping purposes can make selection harder. But that does not mean every system should expose just a handful, or that each API endpoint needs its own tool. Choose boundaries based on the tasks, the model, the available context and the shape of the results, then compare designs on realistic work.
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For a larger collection, group related tools by service or resource and use namespacing to make their roles easier to scan. Avoid both extremes: a sprawling menu of ambiguous actions and a single catch-all tool whose behavior is difficult to predict. The goal is a set the model can select from accurately.
Constrain inputs and define useful results
Use a schema to communicate argument names, types and constraints, then validate arguments again in your application. A schema improves the contract between the model and the tool; it does not replace runtime checks or authorize an operation.
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OpenAI function calling supports strict mode for reliable adherence to a declared schema, but the mode has compatibility requirements. Its documentation says strict schemas must mark all fields as required and set additionalProperties to false; nullable fields can represent values that are optional. Behavior and defaults can vary by API path and model, so check the current configuration you plan to use rather than assuming strictness works identically everywhere. Consult the current OpenAI function-calling guide for the applicable requirements.
Make outputs fit their consumers. When another program will inspect a result, return compact structured data with defined fields instead of prose that the program must parse. Document expected errors and result shapes. If the output cannot be known in advance, a direct result that the model can inspect may be more useful than forcing it into an unsuitable fixed structure. The OpenAI guidance on programmatic tool calling discusses these result-handling choices.
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Keep execution safe and recoverable
Your application remains responsible for what happens after a tool call. Before running an operation, validate its arguments and check the caller’s permissions. For high-impact actions, require application-level approval; do not treat the model’s decision to call a tool as approval.
- Validate at runtime: Check types, ranges, required values and business rules before passing arguments to a service.
- Enforce permissions: Verify the user’s authority for every call, not just when the agent starts a session.
- Plan for retries: Calls can fail, time out or be repeated. Make operations idempotent where feasible so replaying a request does not repeat an unsafe side effect.
- Return actionable errors: Tell the model what failed and what can happen next without creating new side effects in an error handler.
- Gate consequential actions: Put confirmation or approval in the application flow before sensitive or high-impact operations.
These controls are application responsibilities in OpenAI’s programmatic tool-calling guidance and the Agents SDK tools guide. Provider-hosted execution can shift operational responsibilities, so establish which system runs each tool and which safeguards apply.
When not to use a tool
A tool call adds a round trip. For a trivial task, that overhead can exceed the work itself; Anthropic notes that routing lightweight responses through a model may be slower than handling them directly. Use a tool when it supplies needed information, performs a necessary operation or gives the application a useful control point—not merely because a tool is available.
Evaluate tools on real tasks
Tool ergonomics are empirical. Prototype the interface, test it against representative tasks and refine it. Track whether the agent selects the intended tool, provides valid arguments, recovers appropriately from errors and achieves the user’s intended outcome. Include cases where the right action is to ask a clarifying question or not call a tool.
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Quick Recap
A practical design review
- Can the model tell when to use this tool and how it differs from nearby choices?
- Are the arguments clearly described, constrained by a schema and validated by the application?
- Does the result give its next consumer the right information in a usable shape?
- Are authorization, approval, timeout, failure and retry behavior defined?
- Do tests cover realistic tasks, including ambiguity, invalid inputs and recovery?
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