Use tools like remote MCP servers or web search to extend the model's capabilities.
When generating model responses, you can extend capabilities using built‑in tools, function calling, tool search, and remote MCP servers. These enable the model to search the web, retrieve from your files, load deferred tool definitions at runtime, call your own functions, or access third‑party services. Only gpt-5.4 and later models support tool_search.
Web search
Include web search results for the model response
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import OpenAI from"openai";
const client = new OpenAI();
const response = await client.responses.create({
model: "gpt-5",
tools: [
{ type: "web_search" },
],
input: "What was a positive news story from today?",
});
console.log(response.output_text);
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from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-5",
tools=[{"type": "web_search"}],
input="What was a positive news story from today?")
print(response.output_text)
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curl "https://api.openai.com/v1/responses" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-5",
"tools": [{"type": "web_search"}],
"input": "what was a positive news story from today?"
}'
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateWebSearchTool());
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("What was a positive news story from today?"),
]),
], options);
Console.WriteLine(response.GetOutputText());
File search
Search your files in a response
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from openai import OpenAI
client = OpenAI()
response = client.responses.create(
model="gpt-4.1",
input="What is deep research by OpenAI?",
tools=[{
"type": "file_search",
"vector_store_ids": ["<vector_store_id>"]
}]
)
print(response)
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import OpenAI from"openai";
const openai = new OpenAI();
const response = await openai.responses.create({
model: "gpt-4.1",
input: "What is deep research by OpenAI?",
tools: [
{
type: "file_search",
vector_store_ids: ["<vector_store_id>"],
},
],
});
console.log(response);
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using OpenAI.Responses;
string key = Environment.GetEnvironmentVariable("OPENAI_API_KEY")!;
OpenAIResponseClient client = new(model: "gpt-5", apiKey: key);
ResponseCreationOptions options = new();
options.Tools.Add(ResponseTool.CreateFileSearchTool(["<vector_store_id>"]));
OpenAIResponse response = (OpenAIResponse)client.CreateResponse([
ResponseItem.CreateUserMessageItem([
ResponseContentPart.CreateInputTextPart("What is deep research by OpenAI?"),
]),
], options);
Console.WriteLine(response.GetOutputText());
When making a request to generate a model response, you usually enable tool access by specifying configurations in the tools parameter. Each tool has its own unique configuration requirements—see the Available tools section for detailed instructions.
Based on the provided prompt, the model automatically decides whether to use a configured tool. For instance, if your prompt requests information beyond the model’s training cutoff date and web search is enabled, the model will typically invoke the web search tool to retrieve relevant, up-to-date information.
Some advanced workflows can also load additional tool definitions during the interaction. For example, tool search can defer function definitions until the model decides they are needed.
You can explicitly control or guide this behavior by setting the tool_choice parameter in the API request.
