content-extractor

name	content-extractor
description	Extract structured content from web pages using CF Browser MCP tools
user_invocable	true

Content Extractor

Extract content from JavaScript-rendered web pages using CF Browser MCP tools.

Prerequisites

CF Browser MCP server must be configured in .mcp.json (see quick start).

Modes

Analyze the user's intent and select the appropriate mode:

Mode 1: Read Page Content

Trigger: "read this page", "get content from", "what does X say" Tool: browser_markdown Workflow:

1. Call browser_markdown with the URL
2. Clean the markdown (remove navigation, footer boilerplate)
3. Return formatted content with source attribution

Mode 2: Extract Structured Data

Trigger: "extract products", "get prices", "pull data from" Tool: browser_json Workflow:

1. Determine extraction prompt from user intent
2. Call browser_json with URL and extraction prompt
3. Format and return structured JSON

Mode 3: Scrape Specific Elements

Trigger: "get the headlines", "extract article titles", "find all images" Tool: browser_scrape Workflow:

1. Determine CSS selectors from user intent
2. Call browser_scrape with URL and selectors array
3. Post-process and return results

Mode 4: Discover Links

Trigger: "find all links", "list pages on", "discover URLs" Tool: browser_links Workflow:

1. Call browser_links with the URL
2. Filter/categorize links (internal, external, resource)
3. Return organized link list

Mode 5: Visual Capture

Trigger: "screenshot", "show me what X looks like", "capture page" Tool: browser_screenshot Workflow:

1. Call browser_screenshot with URL and optional viewport dimensions
2. Return file path to saved PNG
3. Read the image to show the user

Output Format

• For text: Clean markdown with source URL attribution
• For data: JSON with clear field names
• For images: File path + display
• Truncate if output exceeds 50K characters

Examples

"Read the Hono documentation homepage"
→ browser_markdown("https://hono.dev")

"Extract all pricing tiers from Vercel"
→ browser_json("https://vercel.com/pricing", prompt="Extract all pricing tiers with name, price, and features")

"Get all h2 headings from this blog post"
→ browser_scrape("https://example.com/blog/post", selectors=["h2"])

"Screenshot the landing page at mobile width"
→ browser_screenshot("https://example.com", width=375, height=812)