browser_ai_query
Ask a natural language question about the current page. The AI analyzes the page and provides an intelligent answer. Example: 'Is there a login form on this page?'.
When to use browser_ai_query
Use browser_ai_query when you need to let an AI model understand or act on a page in natural language. It is part of Owl Browser's AI Intelligence toolset and runs inside a self-hosted, source-level stealth engine, so every call inherits the same undetectable browser fingerprint as the rest of your automation — no separate anti-detect setup required.
Usage Example
Parameters
Required
context_idstringrequiredThe unique identifier of the browser context (e.g., 'ctx_000001')
querystringrequiredNatural language question about the page content
Response
Returns a JSON object with the operation result.
{
"success": true,
"result": <value>
}Frequently Asked Questions
What does browser_ai_query do?
Ask a natural language question about the current page. The AI analyzes the page and provides an intelligent answer. Example: 'Is there a login form on this page?'. It belongs to Owl Browser's AI Intelligence category and is available through the REST API, the Python SDK (browser.ai_query()), the Node.js SDK, and the MCP server.
What parameters does browser_ai_query accept?
browser_ai_query accepts 2 required parameters (context_id, query) and 0 optional parameters. All parameters are sent as JSON in a POST request to /api/execute/browser_ai_query.
Is browser_ai_query detectable by anti-bot systems like Cloudflare or DataDome?
No. browser_ai_query executes inside Owl Browser's Chromium engine, which applies fingerprint spoofing at the C++ source level rather than through JavaScript patches. Every tool call shares the same consistent, human-like fingerprint, so anti-bot systems such as Cloudflare, DataDome, and Akamai see an ordinary browser.
Related Tools
browser_summarize_pageGenerate an intelligent, structured summary of the current page using the LLM. Returns key information about the page topic, main content, and interactive elements. Summaries are cached per URL for performance. Much better than raw text extraction for understanding page content.
browser_query_pageAsk a natural language question about the current page content. The LLM analyzes the page and answers questions like 'What is the main topic?', 'Are there any prices listed?', 'Extract all email addresses'. Uses intelligent page summarization for better context understanding.
browser_llm_statusCheck if the LLM is ready to use. When context_id is provided, returns the LLM configuration and status for that specific context (which may use an external LLM provider like OpenAI). When context_id is omitted, returns the global built-in LLM status. Response includes: status ('ready', 'loading', 'unavailable'), type ('builtin', 'external', 'none'), and for external LLMs: endpoint and model name.
browser_nlaExecute complex browser automation using natural language commands. The LLM interprets your instruction and automatically plans and executes multiple browser actions. Examples: 'search for shoes on Amazon', 'log in with test credentials', 'add the first item to cart'.
browser_ai_analyzePerform comprehensive AI analysis of the current page. Returns structured information about the page's topic, main content, structure, and interactive elements.
browser_find_elementFind an element on the page using natural language description. Returns the CSS selector, confidence score, and element details. Useful for locating elements before interaction.