Extracta MCP. Turn messy documents into clean, structured data.
Extracta uses AI to automate data extraction and document classification from PDFs, images, and other files. It lets you define exactly what data you need—like dates, amounts, or vendor names—and then processes entire batches of documents into clean, structured JSON formats using your agent.
Give Claude and any AI agent real-world access
You create and configure data extraction processes by defining precise JSON schemas for the fields you need from documents.
Submit publicly accessible file links (PDF, JPG, PNG) to trigger a background workflow that returns structured JSON data later.
Set up rules that automatically sort incoming documents into predefined types, like invoices or contracts, based on AI analysis.
Retrieve status and structured data for specific documents, including confidence scores and predicted categories.
Update existing extraction settings or view the full configuration of an active document process without creating new endpoints.
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What AI agents can do with Extracta with 10 Tools
These tools let you manage the entire document workflow: defining schemas, uploading files, checking results, and auditing history.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Extracta MCPCreate Classification
Sets up a new document classification model by defining the categories you want to sort documents into (e.g., invoice, receipt).
View Classification
Shows the specific details and settings of an existing document classification...
Get Batch Results
Retrieves historical results for a large number of documents processed through an...
Get Classification Results
Provides the AI's predicted category and confidence score for a specific document.
Create Extraction
Initializes an entire data extraction process, allowing you to specify required...
Delete Extraction
Removes an existing document extraction configuration; this stops all future processing for that setup ID.
Get Results
Checks the current status of a document's extraction job, indicating if it’s still running or complete.
Update Extraction
Modifies mapping rules and field definitions for an already created extraction...
Upload File Url
Submits a link to a document file, kicking off the background job necessary for data...
View Extraction
Displays all settings and current parameters of an existing extraction process...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Extracta, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Extracta. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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No stored credentials
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~60% cost reduction
Copy-pasting data from receipts and invoices is a full-time job.
Today, logging expense reports means opening dozens of PDFs. You click into the total amount field in your spreadsheet, manually copy the date from one corner, and then paste it into another tab. If you're processing 50 documents, that's 200 individual data points moved, copied, and pasted by hand.
With this MCP, the process shifts to a conversation with your agent. You simply tell it: 'I need to extract all dates, amounts, and vendors from these files.' The system handles defining those fields, processing the URLs in the background, and giving you clean JSON data—no copy-pasting required.
Extracta gives you structured document knowledge.
The manual steps that disappear are opening individual documents, figuring out which field is which (is this 'Invoice Date' or 'Payment Due?'), and then cross-referencing data across multiple sheets to ensure accuracy. This takes hours of tedious human review.
Now, you define the schema once and get reliable, auditable results every time. You don't just read text; your agent processes it into usable, structured JSON format.
What Extracta MCP does for your AI
Imagine getting mountains of invoices, receipts, and contracts that all need to be logged into a database. Doing this manually is a nightmare. Extracta changes the game by connecting directly to your AI client, letting you handle complex data extraction through natural conversation. You don't just read; you build the process itself.
You define custom JSON schemas—telling the system exactly which fields matter (like invoice dates or total amounts). Then, simply give it a URL for any document, and it handles the rest. It doesn't just pull text; it classifies documents first, telling you if that file is an 'Invoice' or a 'Receipt,' and then extracts the necessary data into structured JSON.
If you're building out your toolset on Vinkius, this MCP gives you enterprise-grade document processing without needing to write custom API calls every time.
019d7595-3046-730b-8679-4ad1f8eb7998 How to set up Extracta MCP
The bottom line is that your agent handles the entire pipeline, from schema definition to final data output, so you get clean JSON ready for analysis.
First, you define your data needs by setting up a specific extraction process and detailing the required JSON schemas.
Next, you submit one or more publicly accessible document URLs to kick off an asynchronous processing job.
Finally, you poll for results, receiving structured JSON containing the extracted data, its confidence score, and classification details.
Who uses Extracta MCP
Operations managers who are drowning in physical or digital paperwork; data analysts trying to build pipelines that ingest complex documents; and developers needing reliable extraction components. If your job involves converting unstructured files into usable data, this is for you.
Processing batches of vendor invoices by ensuring every required field—like the total amount or payment date—is accurately extracted and logged.
Converting a repository of scanned receipts from various sources into standardized JSON formats to calculate quarterly spending trends.
Building an automated system that ingests incoming client contracts, classifying them immediately and extracting key dates and parties for follow-up workflows.
Benefits of connecting Extracta MCP
Stop manually defining schemas. You tell the system exactly what fields you need—like invoice dates or product totals—and it handles the rest through the create_extraction tool.
You don't wait for manual file uploads. Just give it a URL using upload_file_url, and the background process does the heavy lifting, giving you structured JSON later on.
Classification is built-in. Before extracting data, the system uses document type rules (via create_classification) to ensure you know if the file is an invoice or a contract.
You never lose history. Use get_batch_results to pull records from hundreds of processed documents at once for audit purposes.
Need a quick change? You can use update_extraction to tweak mapping rules on a live process instead of having to build an entirely new setup.
Extracta MCP use cases
Processing Vendor Payments
A finance manager needs to pay vendors using scanned invoices. They ask their agent to use create_extraction first, defining fields like 'vendor name' and 'total amount.' Then, they submit 50 URLs via upload_file_url, getting back structured JSON data ready for payment processing.
Building a Document Library
A legal team receives thousands of client agreements. They use the MCP to define document types using create_classification. The agent processes them, automatically identifying and grouping everything as 'Contract' or 'NDA,' allowing quick auditing.
Tracking Data Changes Over Time
An operations team needs to monitor how many receipts they process each month. They use the get_batch_results tool to fetch a paginated list of all processed documents and associated data payloads for historical review.
Validating New Data Pipelines
A developer needs to test if their new extraction schema works on live files. They use view_extraction to check the configuration, then submit a single URL using upload_file_url, and poll with get_results until they get structured JSON.
Extracta MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Expecting instant results
The user submits an invoice URL via upload_file_url and then immediately tries to read the data using a general command, assuming the AI can retrieve it right away.
Remember that processing runs in the background. After running upload_file_url, you must wait and then use get_results or get_classification_results to check if the job is finished before attempting to read the data.
Skipping schema definition
The user tries to extract amounts from a document without first running create_extraction and defining what 'amount' means in JSON format.
Always define your fields first. Start with create_extraction to establish the rules, then upload documents for processing.
Overwriting necessary settings
The user gets frustrated and attempts to manually re-enter every setting they configured when a small change is needed.
Don't recreate things. Use update_extraction to modify the mapping rules or field definitions on your existing process, saving time.
When to use Extracta MCP
Use this MCP if your core problem involves taking files—like PDFs or scanned images—and converting their content into structured data that a computer can use. You need classification (Is it an invoice?) and extraction (What's the date?). Don't use this if you just need to read simple text from a document; for that, a basic OCR tool will suffice. If your goal is purely workflow automation—like sending emails or setting up calendars—you should look at messaging or calendar integration MCPs instead. This is specialized for transforming messy, unstructured documents into clean JSON.
Frequently asked questions about Extracta MCP
How do I start using Extracta with my documents? +
You first need to run create_extraction to define what data you want. Then, use the upload_file_url tool to submit your files for processing.
Can Extracta tell me if a document is an invoice or something else? +
Yes. You set up rules using create_classification, and then you can use get_classification_results to check the predicted type of any uploaded document.
What happens if I change my extraction requirements after setting it up? +
You don't need to start over. Use the update_extraction tool to modify your existing configuration and mapping rules on the fly.
Does Extracta handle large batches of documents? +
Yes, you use the get_batch_results tool to retrieve historical data from multiple processed files in bulk.
What is the difference between `create_extraction` and `view_extraction`? +
create_extraction sets up a brand new process with defined schemas. view_extraction just shows you all the current settings for an extraction process that already exists.