Parsio MCP. Convert Any Document into Structured JSON Data.

Parsio connects your AI client to a heavy-duty parsing engine, letting you take messy documents—whether they're PDFs, images, or raw emails—and turn them into clean, structured JSON metadata. You don't manually enter invoice numbers or form details anymore; the system handles it all.

Managing Your Data Streams and Templates

First, you need to set up your data pipelines. If you're dealing with a specific type of document flow—say, vendor invoices versus HR records—you use create_mailbox to set up an isolated container for that stream. You can check which containers are active across your account using list_mailboxes. Once you have the mailbox established, you can pull its configuration details and current status by calling get_mailbox.

When you need to understand how data is expected to look, you'll use list_mailbox_templates to see every parsing template set up for that container. If you want deep technical info on one of those templates, run get_template_details; this shows you exactly what fields the parser is designed to capture.

For connecting Parsio to other services, you manage webhooks. You list all existing connections using list_mailbox_webhooks, which helps you keep track of external systems that need data updates when a document arrives. If your system needs an overview of every single parsed record for auditing purposes, run list_parsed_data_history. This gives you a clean log of everything processed in that specific mailbox.

Running the Extraction Jobs

There are different ways to extract data depending on how fast you need the results. If your workflow requires immediate feedback—like validating an ID number right as the user hits send—you use synchronous extraction. For file uploads, you run extract_data_from_file_sync, which instantly pulls structured metadata from that document. Similarly, if you're feeding raw text or HTML directly into the chat interface and need results immediately, you trigger extract_data_from_text_sync.

Both these methods give you instant answers so your agent doesn't stall.

But what if you've got a massive PDF, or you're integrating this via a webhook that can't wait for a chat response? Then you use the asynchronous tools. You kick off background processing for large files using extract_data_from_file_async. If you're passing raw text or HTML content in a chat that needs time to parse, run extract_data_from_text_async.

These jobs run outside of your main conversation thread, so the user experience stays fast.

Once any of these extraction jobs are done—whether they were sync or async—you need the final data. You fetch the structured JSON output from a completed job using get_parsed_document_result. This tool grabs the clean metadata that resulted from the document upload or parsing run. Everything you've submitted, every record parsed by the system, is eventually available for review through list_parsed_data_history, letting you audit exactly what came across those wires.