Airparser MCP. Structured Data from Any Document Type
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Airparser connects your AI client to professional document processing and IDP tools. It lets you automatically pull structured data from PDFs, invoices, emails, and images.
You can manage entire data pipelines—from listing inboxes to creating automated webhooks that push clean JSON results directly into external applications.
What your AI agents can do
Create webhook
Sets up an automatic export trigger whenever a document is successfully processed.
Delete webhook
Removes a previously configured automated data export trigger.
Get document details
Retrieves the structured JSON result for a specific document ID after parsing is complete.
Your agent parses PDFs, images, and emails either immediately or asynchronously for large batches.
You can list all active document inboxes and verify the precise field definitions (schemas) required for extraction.
The MCP creates and deletes webhooks, ensuring that clean JSON data automatically sends to your connected services after parsing is complete.
You retrieve the specific extracted JSON data or check the historical completion status of any document ID.
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Supported MCP Clients
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Airparser: 10 Available Tools
These tools allow you to manage the entire document lifecycle: listing inboxes, validating schemas, parsing files, and setting up automated data exports.
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Start using Airparser on Vinkius019d754bcreate webhook
Sets up an automatic export trigger whenever a document is successfully processed.
019d754bdelete webhook
Removes a previously configured automated data export trigger.
019d754bget document details
Retrieves the structured JSON result for a specific document ID after parsing is complete.
019d754bget inbox details
Gets general metadata about a document inbox, confirming its source and status.
019d754bget inbox schema
Retrieves the specific field definitions (the schema) that dictate what data points will be extracted from documents in an inbox.
019d754blist documents
Lists all document IDs and basic metadata currently stored within a targeted inbox.
019d754blist inboxes
Retrieves a list of every configured Airparser inbox source available in the account.
019d754blist webhooks
Displays all currently active automated data export triggers associated with an inbox.
019d754bparse document async
Starts processing a document in the background, which is ideal for large files or complex batches.
019d754bparse document sync
Immediately processes a single document and returns the extracted data quickly.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Dealing with document data is a series of tedious clicks and copies.
Today, if you need an invoice record, you download the PDF. You open it in one system to find the vendor name, copy that field. Then you switch to another tab to manually input the date, and finally, you cross-reference a third spreadsheet just to get the total amount before pasting everything into your database.
With this MCP, you simply tell your agent: 'Extract all invoice data from this batch.' The system handles the entire process—parsing, validating against the correct schema, and structuring the output. You get clean JSON ready for action.
Airparser gives you automated, structured data exports.
Previously, if a document was processed successfully, your team had to manually go into the system, retrieve the record ID, and then run an export command. This step was often forgotten or missed entirely.
Now, using `create_webhook`, you define that trigger once. When the data is ready, it automatically flows out of the MCP and into your application, closing the loop without human intervention.
What you can do with this MCP connector
This MCP gives your agent the ability to handle complex document processing workflows. Instead of dealing with raw files, you tell your AI client what structure you need, and it manages the extraction process for you. You can list all your configured inboxes—the sources of documents—and check the specific field definitions for each one.
Need to parse a big batch? Use the asynchronous tools to send the document for background processing; otherwise, use the synchronous function for quick checks. Once data is extracted and validated, you can then tell your agent to create webhooks, pushing the clean JSON results directly out to your database or external system via Vinkius's catalog of connections.
019d754b-2e99-73b3-aa4c-39325c43c534 
How Airparser MCP Works
- 1 First, connect your Airparser API Key to this MCP. This authenticates access to all inboxes and processing tools.
- 2 Next, use the agent to list documents or check schemas for a specific inbox to validate the data sources.
- 3 Finally, prompt the agent to run
parse_document_asyncorcreate_webhookto execute the extraction or automate the export.
The bottom line is you use natural conversation to manage complex, multi-step document pipelines that normally require manual API calls and multiple dashboards.
Who Is Airparser MCP For?
Operations managers dealing with high volumes of receipts or invoices. Developers building custom data ingestion backends. Recruiters who need to process hundreds of resumes into a searchable format.
Manages accounts payable by instructing the agent to list documents and trigger webhook exports for all new invoices.
Integrates document parsing into custom Python workflows, using tools like get_inbox_details to build validation checks before running extraction.
Processes large batches of resumes by listing inboxes and then requesting the agent to parse documents asynchronously for structured JSON data.
What Changes When You Connect
- Stop manually checking job statuses. You can use
get_document_detailsto check the full JSON result of any document ID, giving you instant visibility into the extracted data. - Process high volumes without timeouts. For big batches or complex files, use
parse_document_async. Your agent handles the background work and reports back when it's done. - Build reliable pipelines. Use
create_webhookto automatically push parsed JSON data to your target application every time a document lands in an inbox—no manual triggers required. - Verify inputs before running jobs. Run
get_inbox_schemafirst. This confirms the extraction field definitions match exactly what your database needs. - Manage sources simply. You can use
list_inboxesto see all data streams andget_inbox_detailsto confirm the metadata for a specific source.
Real-World Use Cases
The AR team needs structured candidate data.
Instead of manually opening hundreds of PDFs, the agent runs list_inboxes to find the 'Resumes' folder. It then uses parse_document_async on all files in that inbox and finally asks for a filtered list using get_document_details.
Accounts Payable needs automated invoice routing.
The Ops Manager connects the MCP, runs create_webhook, linking it to the accounting system. Now, every new PDF landing in the 'Invoices' inbox automatically triggers a clean JSON record without any manual intervention.
A developer needs to build a validation layer.
Before saving data, the agent first uses get_inbox_schema to validate that 10 required fields exist. If they don't match, the process stops and alerts the developer before bad data gets into production.
The Tradeoffs
Assuming immediate results for large files
The user tries to run a massive batch of 500 PDFs using parse_document_sync and the call fails or times out.
→
Always use parse_document_async when dealing with more than a handful of documents. This offloads the heavy lifting, allowing your agent to monitor the status until completion.
Forgetting to check data sources
The developer runs extraction against an inbox that was recently reconfigured or renamed, leading to missing fields.
→
Before parsing, run list_inboxes and then use get_inbox_details alongside get_inbox_schema. This verifies the source is active and the schema is current.
Relying on simple file uploads
The user just attaches a PDF to the chat, expecting structured data without defining the fields first.
→
You must define the extraction requirements. Use get_inbox_schema to validate the desired output structure before triggering any parsing functions.
When It Fits, When It Doesn't
Use this MCP if your problem is taking unstructured files (PDFs, images) and turning them into structured data that needs to be stored or acted upon by a downstream system. You need reliable pipelines, not just one-off reads. If you only need to read metadata from a PDF once and never want it automated, simple file reading tools are fine. But if the process involves confirming what fields exist (get_inbox_schema), processing large batches over time (parse_document_async), or sending data somewhere else automatically (create_webhook), then this MCP is necessary.
Common Questions About Airparser MCP
How do I list all document sources with Airparser? (list_inboxes) +
Run list_inboxes. This command shows you every inbox configured in your account, giving you a clear overview of all the document types you are managing.
What is the difference between parse_document_async and parse_document_sync? (parse_document_async) +
Use parse_document_async for large files or batches because it runs in the background. Use parse_document_sync only when you need an immediate result for a single document.
Can I check what fields are expected by Airparser? (get_inbox_schema) +
Yes, use get_inbox_schema. This shows the exact definitions and data types required for documents in a given inbox, so you know exactly what to expect.
How do I confirm that parsing actually worked? (get_document_details) +
After processing, use get_document_details with the document ID. This retrieves the full structured JSON payload, confirming all data points were captured successfully.
How do I use list_documents to see what documents are in a specific inbox? +
Use list_documents to pull metadata for every item residing in an inbox. This lets you check the document IDs and names before deciding which ones need processing.
What is the process for managing automated data exports using list_webhooks or delete_webhook? +
You run list_webhooks to view all active export connections tied to an inbox. If you need to remove one, call delete_webhook with the webhook's specific ID.
Where can I find general setup information about a document source using get_inbox_details? +
Run get_inbox_details when you need background context on an inbox. This retrieves overall metadata, such as the official name and intended purpose of that particular data source.
How do I set up automatic data exports using create_webhook? +
You establish automated data pipelines by calling create_webhook. This function sends the parsed JSON results directly to an external application, automating your workflow immediately after processing.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.