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