Parseflow
ParseFlow is an AI-driven all-in-one document parsing library that supports PDF, Word, Excel, PPT, and image OCR, provides semantic search and batch processing functions, and includes an MCP Server for AI assistants to use.
rating : 2.5 points
downloads : 6.8K
What is the ParseFlow MCP Server?
The ParseFlow MCP Server is a document parsing service based on the Model Context Protocol (MCP). It allows AI assistants (such as Claude Desktop, Windsurf, Cursor, etc.) to directly access and parse various document formats, including PDF, Word, Excel, PowerPoint, and images. Through the MCP protocol, AI assistants can call 23 tools of ParseFlow to extract text, search for content, process documents, etc., without users having to manually upload or preprocess files.How to use the ParseFlow MCP Server?
Using the ParseFlow MCP Server is very simple: 1) Install the MCP Server (via npm or npx), 2) Add the ParseFlow server configuration to the configuration file of the AI assistant (such as Claude Desktop), 3) Restart the AI assistant, 4) Directly use the document processing function in the conversation. The AI assistant will automatically recognize the document path and call the corresponding parsing tool.Use Cases
The ParseFlow MCP Server is particularly suitable for the following scenarios: • Research work that requires analyzing a large number of documents • Extracting key data from PDF reports • Searching for specific information in Word documents • Parsing Excel tables for data analysis • Extracting content from PowerPoint presentations • Recognizing text in images (OCR) • Batch processing multiple documents • Document library management that requires semantic searchMain Features
PDF Document Parsing
Supports multiple PDF parsing strategies, including raw text extraction, formatted text extraction, and cleaned text. Supports encrypted PDFs (password required), page-by-page extraction, full-text search, image extraction, table of contents retrieval, etc.
PDF Document Manipulation
Provides functions such as PDF merging, splitting, extracting specified pages, adding text/image watermarks, and removing watermarks to meet various PDF processing needs.
Office Document Parsing
Fully supports Microsoft Office documents: Word (text and HTML extraction), Excel (multi-sheet data extraction), PowerPoint (slide content extraction).
Image OCR Recognition
Supports image text recognition in 12 languages, can extract text content from images and perform searches, and supports common image formats.
Semantic Search
Intelligent document search based on AI vector embedding, no need for exact keyword matching, can understand the semantic meaning of the query and find relevant content.
Batch Processing
Supports parallel processing of multiple files, can recursively scan directories, batch extract and search document content, and improve processing efficiency.
MCP Protocol Integration
Provides 23 tools through the Model Context Protocol and seamlessly integrates with mainstream AI assistants, including Claude Desktop, Windsurf, Cursor, etc.
Advantages
One-stop solution: Supports multiple formats such as PDF, Word, Excel, PPT, and image OCR
Friendly to AI assistants: Directly integrated through the MCP protocol without an additional interface
Comprehensive functions: Provides 23 tools covering various aspects such as parsing, searching, and operating
Easy to use: Can be used in AI assistants with simple configuration
Open source and free: Based on the MIT license, can be freely used and modified
Continuously updated: Regularly adds new functions and improvements
Limitations
Requires local installation: Node.js and npm need to be installed on the user's computer
Large file processing: Processing very large documents may take a long time
OCR accuracy: OCR recognition accuracy is affected by image quality
Format compatibility: Some special-format Office documents may not be fully parsed
Memory usage: May consume a large amount of memory when processing a large number of documents
How to Use
Install the ParseFlow MCP Server
Globally install the ParseFlow MCP Server via npm, or directly run it using npx.
Configure the AI Assistant
Add the ParseFlow MCP Server configuration to the configuration file of the AI assistant (such as Claude Desktop).
Restart the AI Assistant
Restart the AI assistant to load the new MCP Server configuration.
Start Using
In the conversation of the AI assistant, you can directly use the document processing function, for example: 'Please analyze this PDF document: /path/to/document.pdf'
Usage Examples
Academic Research Document Analysis
Researchers need to analyze multiple academic papers in PDF format, extract key information, and conduct comparative analysis.
Enterprise Report Data Extraction
Business analysts need to extract financial data from multiple Excel and Word reports for quarterly performance analysis.
Digitization of Image Documents
Archivists need to convert scanned image documents into searchable text and establish an index.
Multi-document Semantic Search
Legal assistants need to find relevant precedents and clauses in a large number of legal documents.
Frequently Asked Questions
Which AI assistants does the ParseFlow MCP Server support?
Which dependencies need to be installed?
How to process encrypted PDF documents?
Which languages does OCR support?
Can multiple files be processed in batches?
How does semantic search work?
Is ParseFlow free?
How to get help when encountering problems?
Related Resources
ParseFlow Core npm Package
The npm page of the ParseFlow core library, containing detailed usage documentation and API references.
ParseFlow MCP Server npm Package
The npm page of the ParseFlow MCP Server, containing installation and usage instructions.
GitHub Repository
The source code repository of ParseFlow, containing the latest code, Issues, and contribution guidelines.
Model Context Protocol Documentation
The official documentation of the MCP protocol, to understand the working principle and specifications of MCP.
Claude Desktop Configuration Guide
The official documentation of Claude Desktop, containing the MCP Server configuration instructions.

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