Parseur MCP Server
Automate document processing via Parseur — list mailboxes, upload PDFs/Emails, extract structured data pipelines, and trigger template logic natively.
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What is the Parseur MCP Server?
The Parseur MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Parseur via 10 tools. Automate document processing via Parseur — list mailboxes, upload PDFs/Emails, extract structured data pipelines, and trigger template logic natively. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (10)
Tools for your AI Agents to operate Parseur
Ask your AI agent "Check my Parseur mailboxes to find the specific bounding IDs." and get the answer without opening a single dashboard. With 10 tools connected to real Parseur data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
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Parseur MCP Server capabilities
10 toolsThe type determines the parsing engine (e.g., "pdf", "email", "attachment"). Once created, you can configure templates and forward documents to the mailbox for automatic extraction. Create a new Parseur mailbox for document parsing
Pass the template name and a JSON config string defining field mappings. Parseur will use this template to extract structured data from matching documents. Create a new extraction template for a Parseur mailbox
Fields depend on the template configuration (e.g., invoice_number, total_amount, line_items). Only works for documents with status "processed". Retrieve the fully extracted JSON data from a parsed document
Does not include the parsed data itself — use get_document_data for that. Get metadata of a single parsed document
Use this to verify mailbox setup before sending documents. Get detailed configuration of a specific Parseur mailbox
Each entry includes document ID, status (processed, failed, pending), and metadata like sender and received date. List all parsed documents inside a Parseur mailbox
Each mailbox represents a parsing pipeline for a specific document type (invoices, receipts, emails). Use the returned mailbox IDs for subsequent operations like listing documents or uploading files. List all Parseur parsing mailboxes
Templates define the extraction rules (field names, locations, regex patterns) used to pull structured data from incoming documents. List available extraction templates for a Parseur mailbox
Useful after fixing template rules or when the original parse failed due to a transient error. The document will be matched against the latest template rules. Retry parsing a failed or errored Parseur document
eml) to the specified mailbox for automatic parsing. The document enters the processing queue and will be parsed according to the mailbox template. Returns the new document ID for tracking. Upload a document URL to a Parseur mailbox for parsing
What the Parseur MCP Server unlocks
Bring Parseur Document Extraction arrays directly into your AI workflows. By explicitly mapping into powerful OCR and templating engines, your agent can push unstructured PDFs or bulk emails into remote routing limits, parsing exact text fields securely. Extract fields, examine documents, list defined parse-templates, and retry pipelines without manual intervention.
What you can do
- Mailboxes & Templates — Examine specifically bound mailboxes tracking which explicit templates dictate data extraction limits mapped natively
- Document Navigation — Extract properties showing precisely which unstructured strings were identified inside uploaded payloads checking
status: parsedcorrectly - Payload Uploading — Instruct the node limits mapping
upload_documentgenerating raw payloads routing straight into the engine for OCR logic - Job Management — Discover disconnected states mitigating failed validations by pushing
retry_documentinstantly forcing physical pipeline resets
How it works
1. Subscribe to this server
2. Enter your Parseur API Key explicitly
3. Start evaluating explicit AI OCR workflows reliably via Claude, Cursor, or any MCP structure
Who is this for?
- Operational Workflows — explicitly map invoicing pipelines investigating extraction errors remotely avoiding web limits
- Finance Teams — trace OCR receipts dynamically identifying missing fields from Chat architectures directly into a JSON limit natively
- Developers — route manual document loads simulating continuous AI logic mapping testing integrations on webhook bounds
Frequently asked questions about the Parseur MCP Server
Does this tool parse the document directly or use the cloud engine?
The tool offloads the logic specifically via endpoints mapping back to the Parseur Cloud Engine. The AI acts to organize mailboxes, list templates, and fetch final states securely without computing massive local OCR networks.
Can I upload a raw file string to be parsed?
Yes. Utilizing the explicitly mapped upload_document constraint, the agent can inject raw string boundaries identifying formatting, passing files straightforward into the target mailbox ID natively.
Will I see missing required fields if extraction fails?
Absolutely. Querying get_document_details lists specific status bounds. If a template expects InvoiceTotal and misses it, the document flags a processing boundary issue precisely traceable here.
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