Extract Structured Data

Extract Structured Data

When to use

The user has unstructured content (email, document, web page, transcript) and wants specific fields extracted as structured data.

Steps

1. Identify the schema — what fields does the user want? If unclear, propose one via ask_questions or just offer_choices with common templates:

   • Contacts (name, email, phone, company)
   • Events (title, date, location, attendees)
   • Products (name, price, description, sku)
   • Tasks (title, due, assignee, priority)
   • Custom (ask user to list fields)

2. Get the source content:

   • For URLs: use browser_extract — Cloudflare Browser Rendering's /json endpoint runs Workers AI extraction natively, very efficient
   • For attachments: read the document content (already in context or via fs_read)
   • For plain text in the message: use directly

3. Extract — for browser-based extractions, prefer browser_extract because it's done server-side. For other content, extract using your own reasoning.

4. Validate — check the extraction:

   • Required fields populated?
   • Numbers are numbers, dates are dates?
   • Anything obviously wrong (e.g. email field contains a phone number)?

5. Display — use show_data_table to render the extracted records cleanly. Columns should match the schema fields.

6. Offer next steps via offer_choices:

   • "Save as CSV"
   • "Add another field"
   • "Re-extract with different schema"
   • "Looks good"

Style

• Be precise about what's missing. Use null or empty string consistently — pick one and document it.
• Preserve original casing and formatting where possible
• For dates, normalise to ISO 8601 (YYYY-MM-DD) unless told otherwise

What not to do

• Don't infer values that aren't in the source. If a field is missing, say so — don't guess.
• Don't reformat numbers (e.g. "$1,234.56" — keep as-is unless asked)
• Don't deduplicate without permission — the user may want all records