# Example Requests Use this Copilot Scraper API example to send a basic prompt with the NetNut LLM Scraper. This page shows a cURL request, a sample Copilot response, and the main response fields returned by the API. Copilot performs AI-powered web retrieval and synthesis, returning a structured answer with citations, inline citations, and structured tables. *** #### cURL Request ```bash curl -X POST https://llm-scraper.netnut.io/search \ -U "username:password" \ -H "Content-Type: application/json" \ -d '{ "engine": "copilot", "mode": "chat", "prompt": "what are the leading SEO tips in 2026" }' ``` *** #### Example Copilot Response Example shape – your actual traceID, timestamps, and content will vary. ```json { "timestamp": "2026-04-20T12:19:21.231Z", "traceID": "a1b2c3d4-e5f6-7a8b-9c0d-e1f2a3b4c5d6", "request_duration": 14.28, "process_duration": 14.28, "scraper": "Copilot", "response": { "prompt": "what are the leading SEO tips in 2026", "text": "The leading SEO tips for 2026 center on AI-readiness, entity-based optimization, technical excellence, and delivering deeply useful, intent-matched content. Winning in 2026 means helping AI interpret your expertise: build topic clusters and entity relationships, use structured data and schema, and create passage-ready content so LLMs can extract accurate snippets. Strengthen foundational SEO by improving crawlability, internal linking, and E-E-A-T signals. Refresh existing content before creating new — updating older pages often produces faster ranking gains. Improve Core Web Vitals and optimize for mobile-first indexing.", "text_markdown": "**The leading SEO tips for 2026 center on AI-readiness, entity-based optimization, technical excellence, and delivering deeply useful, intent-matched content.**\n\n### 1. Optimize for AI-Driven Search & LLM Discovery\n- Build **topic clusters** and **entity relationships** instead of isolated keyword pages.\n- Use **structured data** and schema to make content easy for AI to cite.\n- Create **passage-ready content** so LLMs can extract accurate snippets.\n\n### 2. Strengthen Foundational SEO (Technical + E-E-A-T)\n- Improve crawlability, internal linking, and site architecture.\n- Demonstrate **E-E-A-T** to become a trusted source for both humans and AI.\n\n### 3. Refresh Existing Content Before Creating New\n- Prioritize pages already ranking on page 2 or low page 1 for quick wins.\n\n### 4. Enhance Technical Performance & User Experience\n- Improve **Core Web Vitals** (speed, interactivity, stability).\n- Optimize for mobile-first indexing.", "citations_found": true, "citations": [ { "id": 1, "url": "https://moz.com/blog/top-seo-tips-2026-whiteboard-friday", "title": "Top SEO Tips For 2026 - Moz", "section": "citations" }, { "id": 2, "url": "https://sitebulb.com/resources/guides/seo-in-2026-17-expert-tips-predictions/", "title": "SEO in 2026: 17 Expert Tips & Predictions - Sitebulb", "section": "citations" }, { "id": 3, "url": "https://www.bay20.com/10-practical-seo-tips-that-still-work-in-2026/", "title": "10 Practical SEO Tips That Still Work in 2026 - bay20.com", "section": "citations" }, { "id": 4, "url": "https://www.ranktracker.com/blog/improve-seo-2026-guide/", "title": "How to Improve SEO in 2026: A Complete Guide to Boost Rankings, Traffic ...", "section": "citations" } ], "inline_citations": [ { "id": 1, "url": "https://moz.com/blog/top-seo-tips-2026-whiteboard-friday", "title": "Top SEO Tips For 2026 - Moz", "text_anchor": "Build topic clusters and entity relationships instead of isolated keyword pages. Use structured data and schema to make content easy for AI to cite." }, { "id": 2, "url": "https://sitebulb.com/resources/guides/seo-in-2026-17-expert-tips-predictions/", "title": "SEO in 2026: 17 Expert Tips & Predictions - Sitebulb", "text_anchor": "Improve crawlability, internal linking, and site architecture. Demonstrate E-E-A-T to become a trusted source for both humans and AI." } ], "tables": [ { "title": "Quick Comparison: What Matters Most in 2026", "table_columns": ["Priority", "Why It Matters"], "table_rows": [ [{"value": "AI & Entity Optimization"}, {"value": "AI search relies on semantic understanding"}], [{"value": "Technical SEO"}, {"value": "Crawlability + speed directly impact rankings"}], [{"value": "E-E-A-T"}, {"value": "AI and users trust authoritative sources"}], [{"value": "Content Refreshing"}, {"value": "Faster ranking gains than new content"}], [{"value": "User Experience"}, {"value": "Mobile-first indexing + engagement signals"}] ] } ] } } ``` *** #### Response Fields In this Copilot Scraper response: * `engine` selects the Copilot scraper through the engine request field. * `mode` sets the Copilot chat mode used for the request. * `prompt` contains the original prompt sent to the LLM Scraper API. * `text` returns the plain-text Copilot answer. * `text_markdown` returns the same answer in Markdown format. * `citations_found` shows whether Copilot returned source citations. * `citations` lists the source URLs and titles used in the response. * `inline_citations` maps each source to the specific text span it supports. * `tables` contains structured tables extracted from the Copilot response. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://help.netnut.io/netnut-documentation/netnut-scraper-apis/llm-scraper/copilot/example-requests.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.