URL context

The URL context tool lets you provide additional context to the models in the form of URLs. By including URLs in your request, the model will access the content from those pages (as long as it's not a URL type listed in the limitations section) to inform and enhance its response.

The URL context tool is useful for tasks like the following:

  • Extract Data: Pull specific info like prices, names, or key findings from multiple URLs.
  • Compare Documents: Analyze multiple reports, articles, or PDFs to identify differences and track trends.
  • Synthesize & Create Content: Combine information from several source URLs to generate accurate summaries, blog posts, or reports.
  • Analyze Code & Docs: Point to a GitHub repository or technical documentation to explain code, generate setup instructions, or answer questions.

The following example shows how to compare two recipes from different websites.

Python

# This will only work for SDK newer than 2.0.0
from google import genai

client = genai.Client()

url1 = "https://www.foodnetwork.com/recipes/ina-garten/perfect-roast-chicken-recipe-1940592"
url2 = "https://www.allrecipes.com/recipe/21151/simple-whole-roast-chicken/"

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input=f"Compare the ingredients and cooking times from the recipes at {url1} and {url2}",
    tools=[{"type": "url_context"}]
)

# Print the model's text response and its source annotations
for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.type == "text":
                print(content_block.text)
                if content_block.annotations:
                    print("\nSources:")
                    for annotation in content_block.annotations:
                        if annotation.type == "url_citation":
                            print(f"  - {annotation.title}: {annotation.url}")

Javascript

// This will only work for SDK newer than 2.0.0
import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

async function main() {
  const interaction = await client.interactions.create({
    model: "gemini-3.7-flash",
    input: "Compare the ingredients and cooking times from the recipes at https://www.foodnetwork.com/recipes/ina-garten/perfect-roast-chicken-recipe-1940592 and https://www.allrecipes.com/recipe/21151/simple-whole-roast-chicken/",
    tools: [{ type: "url_context" }]
  });

  // Print the model's text response and its source annotations
  for (const step of interaction.steps) {
    if (step.type === 'model_output') {
      for (const contentBlock of step.content) {
        if (contentBlock.type === 'text') {
          console.log(contentBlock.text);
          if (contentBlock.annotations) {
            console.log("\nSources:");
            for (const annotation of contentBlock.annotations) {
              if (annotation.type === 'url_citation') {
                console.log(`  - ${annotation.title}: ${annotation.url}`);
              }
            }
          }
        }
      }
    }
  }
}

await main();

REST

# Specifies the API revision to avoid breaking changes when they become default
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
      "model": "gemini-3.7-flash",
      "input": "Compare the ingredients and cooking times from the recipes at https://www.foodnetwork.com/recipes/ina-garten/perfect-roast-chicken-recipe-1940592 and https://www.allrecipes.com/recipe/21151/simple-whole-roast-chicken/",
      "tools": [{"type": "url_context"}]
  }'

How it works

The URL Context tool uses a two-step retrieval process to balance speed, cost, and access to fresh data. When you provide a URL, the tool first attempts to fetch the content from an internal index cache. This acts as a highly optimized cache. If a URL is not available in the index (for example, if it's a very new page), the tool automatically falls back to do a live fetch. This directly accesses the URL to retrieve its content in real-time.

You can combine the URL context tool with other tools to create more powerful workflows.

Gemini 3 models support combining built-in tools (like URL Context) with custom tools (function calling). Learn more on the tool combinations page.

When both URL context and Grounding with Google Search are enabled, the model can use its search capabilities to find relevant information online and then use the URL context tool to get a more in-depth understanding of the pages it finds. This approach is powerful for prompts that require both broad searching and deep analysis of specific pages.

Python

# This will only work for SDK newer than 2.0.0
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input="Give me three day events schedule based on YOUR_URL. Also let me know what needs to taken care of considering weather and commute.",
    tools=[
        {"type": "url_context"},
        {"type": "google_search"}
    ]
)

for step in interaction.steps:
    if step.type == "model_output":
        for content_block in step.content:
            if content_block.