Move PDF tables into Excel

The problem

Copying a table from a PDF can mix up its rows and columns, leaving your agent with unreliable information. A scanned table may be unreadable to it altogether. The agent needs tools that can read the table, keep its cells in order, and place them in an Excel file.

How NativePort helps

NativePort uses Mistral’s document-reading service to read a table inside a PDF and collect its rows and columns. Your agent can then use those results to create an Excel file for you. This saves you from retyping the table, while letting you check the spreadsheet against the original.

Let your agent build it

You don’t need to write this code yourself. Copy the prompt and paste it into your agent. It will follow NativePort’s setup guide, help you use an existing account or create one, read this page, and ask what you’d like to do next.

View prompt

Help me use NativePort for this use case.

1. Read https://nativeport.ai/SKILL.md and follow its instructions to learn how to use NativePort. Save or update the skill if your environment supports it.

2. Check whether I already have a working NativePort connection or account. Reuse it if available. Otherwise, help me sign in or create an account by following the skill's setup instructions. Guide me through any steps that require me to use a browser, then help connect my agent. Keep credentials private.

3. Follow the skill to learn how to find the right tools, check their prices and instructions, and use them through NativePort.

4. Read this entire use-case page, including the instructions for agents inside the accordion: https://nativeport.ai/use-cases/pdf-tables-to-excel/

Once setup is complete and you have read the page, briefly explain what you can help me do and ask me what I would like to do next. Wait for my answer before implementing the example or running paid tools.

Instructions for the agents - You don't need to read this

Technical implementation

Use Python 3 with requests installed (python -m pip install requests). Set NATIVEPORT_API_KEY in your environment to your NativePort key. Run the snippets on your server, where your key stays private.

Set DOCUMENT_URL to an HTTPS URL the document service can fetch, such as a time-limited file link. The example reads page 0 (the first page). Select only pages you need, up to 100 per request; split longer documents into batches. Send the URL, not inline file bytes.

Also install openpyxl (python -m pip install openpyxl) to write the workbook locally. Adapt this line-item schema to your table.

python

import os
import requests

BASE = "https://api.nativeport.ai"
HEADERS = {"Authorization": f"Bearer {os.environ['NATIVEPORT_API_KEY']}"}

payload = {
    "document": {"type": "document_url", "document_url": os.environ["DOCUMENT_URL"]},
    "pages": [0],
}
schema = {'type': 'object',
 'properties': {'rows': {'type': 'array',
                         'items': {'type': 'object',
                                   'properties': {'description': {'type': ['string',
                                                                           'null']},
                                                  'quantity': {'type': ['string',
                                                                        'null']},
                                                  'amount': {'type': ['string',
                                                                      'null']}},
                                   'required': ['description',
                                                'quantity',
                                                'amount'],
                                   'additionalProperties': False}}},
 'required': ['rows'],
 'additionalProperties': False}
payload["document_annotation_format"] = {
    "type": "json_schema",
    "json_schema": {"name": "extraction", "strict": True, "schema": schema},
}
payload["document_annotation_prompt"] = (
    'Extract only what is visible. Use null for missing or unreadable values.'
)
response = requests.post(BASE + "/mistral/v1/ocr", headers=HEADERS,
                         json=payload, timeout=120)
response.raise_for_status()
result = response.json()
import json
fields = result.get("document_annotation")
if isinstance(fields, str):
    fields = json.loads(fields)
if fields is None:
    raise ValueError("No document annotation returned; inspect the OCR response")
from openpyxl import Workbook

workbook = Workbook()
sheet = workbook.active
sheet.title = "Extracted rows"
columns = ["description", "quantity", "amount"]
sheet.append(columns)
for row in fields["rows"]:
    sheet.append([row.get(column) for column in columns])
    # Keep extracted strings as text, including values starting with '='.
    for cell in sheet[sheet.max_row]:
        if isinstance(cell.value, str):
            cell.data_type = "s"
workbook.save("extracted-table.xlsx")

NativePort returns structured data; your script creates extracted-table.xlsx. This preserves recognized amounts as text. Check rows against the PDF, then convert quantities and amounts to numbers using the correct locale. An annotation request uses the Document AI rate. See the Mistral request policy for document URLs, page selection, and accepted options. OCR returns recognized content; check unclear scans against the source.

Tool costs

Tool used in the example	Price per call
Mistral — structured extraction	$0.005275 / call (1 page)

Prices in USD. Usage-based tools have no fixed per-call price. View pricing.

The problem

How NativePort helps

Let your agent build it

Technical implementation

Tool costs

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