$ loading_
Convert PDFs and images into Markdown, OCR text, and structured invoice fields.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "structdoc-mcp" yet — see the docs or source repo.
Parse this invoice image into structured data. Extract the issue date, total amount, tax amount, merchant name, and invoice number, and return them as JSON.
Structured JSON with key invoice fields for reimbursement or bookkeeping.
Convert this PDF document into Markdown, preserving heading levels, paragraphs, and list structure as much as possible.
Editable Markdown text for further organization or AI processing.
Recognize the text in this image, output the full OCR text, and organize it into paragraphs.
Readable text extracted from the image for search, archiving, or analysis.
Developers can first convert PDFs or images into Markdown or OCR text, then pass the results to AI agents for summarization, classification, or Q&A. This reduces the difficulty of handling raw files directly.
Office workers or data analysts can extract structured fields from invoice or receipt images for reimbursement, reconciliation, or data entry. It is more suitable for batch processing than manual input.
Teams that need metered access to document parsing can use this MCP service to provide pay-per-call capabilities to AI agents. The description mentions x402 with USDC on Base/Solana for payments.
It is an MCP server for StructDoc that converts PDFs and images into structured data. Known outputs include Markdown, OCR text, and invoice/receipt fields.
Based on the provided description, inputs include PDFs and images. Outputs include Markdown, OCR text, and structured invoice or receipt fields.
The description says it supports pay-per-call access via x402, using USDC on Base or Solana. For exact setup details, see the source repository.
Extract structured document data and classify files through a hosted MCP server.
Efficiently extracts structured data from complex documents and Excel with lower token usage.
Convert documents into structured data and generate files with caching support.
Turn unstructured documents into a searchable knowledge base for AI agents.
Extract text and parse document layouts through PaddleOCR via MCP.
Converts research document folders into structured files for AI analysis.