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You can give an AI agent a useful capability by connecting it to one small function that your application defines and runs. For a first project, choose a predictable task, describe its inputs with a schema, attach the function to an agent, and test a request that should call it. OpenAI’s Agents SDK guide calls its quickstart “the shortest path to a working SDK integration,” but the documentation does not establish that every beginner can finish in 15 minutes. Treat that time as a challenge, not a promise; you’ll need basic coding familiarity and a working development and SDK/API setup.
What you are building
An agent combines instructions, a model, and tools. A function tool gives the agent access to a specific piece of application logic: the model can request the tool, but your application’s execution function does the work and returns a result. The OpenAI SDK documentation describes tools as a way to extend an agent with capabilities such as calling application functions or interacting with other systems. OpenAI Agents SDK guide and Agents SDK documentation
Keep the first tool narrow. For example, have it look up one value in fixed sample data or perform a safe calculation. A single, well-defined input and output is easier to understand and check than an assistant that can take broad actions.
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Choose a local function for your first tool
For a first local capability, a function tool is the direct route: your app defines the tool and its execution code. The JavaScript documentation shows a tool with a name, a description, a Zod parameter schema, and an asynchronous execute function. Its example is named get_weather and accepts a city parameter; it illustrates the structure, not a verified live weather service. OpenAI guide to using tools
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Make the name recognizable, describe when the tool should be used, and keep the parameter schema small. Validate inputs and make the function’s behavior predictable. The model decides whether to request the tool based on the conversation and tool definition; your application executes the code and supplies the result.
Build and test the first agent
- Pick one operation. Decide what information the function needs and what it will return. Prefer a read-only lookup or safe calculation over an action that changes data or affects another person.
- Define the function tool. In JavaScript, follow the documented pattern: provide a tool name and description, define parameters with a Zod schema, and implement the asynchronous
executefunction. Ensure the function handles invalid or missing inputs deliberately. - Define one agent and attach the tool. Configure the agent with instructions, a model, and the function tool. The SDK places tool wiring in the agent definition and workflow; hosted tools, function tools, and hosted MCP tools can be attached to an agent.
- Run a request designed to use the tool. Ask a question whose answer requires the operation you chose. For a lookup tool, provide the kind of key its schema accepts. Inspect the execution trace to see whether the agent requested the tool, what arguments it supplied, and what result came back.
- Check the result and errors. Compare the returned answer with the function’s result. Test an invalid input and a request that should not need the tool, too. Fix unclear instructions, schema mismatches, or function errors before adding more capabilities.
The SDK quickstart lays out the short integration path as installing the SDK, defining an agent, and running it. Follow the current setup instructions in the Agents SDK guide for the language and environment you are using. This guide does not claim a specific run result or guarantee a completion time.
How to debug a tool call
When the agent does not behave as expected, inspect the run and tool-call trace rather than guessing. Check whether the model selected the tool, whether the arguments match the schema, whether the execution function returned the expected value, and how that result appears in the final response. OpenAI’s SDK guide points to integrations and observability for inspecting runs, and recommends evaluations after tracing. OpenAI Agents SDK guide
Do you need MCP to give an agent a tool?
No. A local function tool is enough when your application owns the code and can execute it directly. MCP is a server-mediated option: an MCP server publishes tool definitions and runs the calls, while the Agents API discovers the tools, invokes the server, and receives results. Use MCP when the capability is already exposed by a server or a standardized server connection fits your setup—not as a prerequisite for a first local function. OpenAI guide to MCP connections
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| Choice | Where the tool is defined and runs | When it fits |
|---|---|---|
| Function tool | Your application defines and executes the function. | A small local capability you want to implement and control directly. |
| MCP server tool | An MCP server publishes definitions and executes calls; the Agents API connects to it. | A capability already available through a server or a workflow that calls for a server connection. |
The documentation describes these integration patterns, not a quantitative performance comparison. The OpenAI documentation’s anonymous HTTP MCP example should not be taken to mean that other MCP servers are anonymous or require no authentication.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to do before expanding the demo
A successful tool call proves only that the basic path works; it does not establish that an agent is ready for consequential use. Before enabling write-capable or otherwise sensitive actions, add safeguards appropriate to the risk, including review or approval where needed. Trace behavior and create a small evaluation baseline before optimizing model choice, cost, or latency.
OpenAI’s practical guide recommends establishing evaluations and an accuracy baseline, then considering cost and latency optimizations. It also describes starting with one agent and adding tools incrementally, which can keep the workflow easier to evaluate and maintain. More agents are not automatically better: add orchestration only when distinct task boundaries or workflow requirements justify it. OpenAI, A practical guide to building agents
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