mcp-document-converter and mcp_documents_reader

The MCP Documents Reader and MCP Document Converter are complements within the Model Context Protocol ecosystem, where the reader allows AI agents to consume multiple document types, and the converter enables AI agents to transform these documents between formats, both operating on the same underlying protocol.

mcp-document-converter
50
Established
mcp_documents_reader
50
Established
Maintenance 10/25
Adoption 5/25
Maturity 20/25
Community 15/25
Maintenance 10/25
Adoption 6/25
Maturity 20/25
Community 14/25
Stars: 9
Forks: 4
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 15
Forks: 3
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No risk flags
No risk flags

About mcp-document-converter

xt765/mcp-document-converter

MCP Document Converter - A powerful MCP tool for converting documents between multiple formats, enabling AI agents to easily transform documents.

This tool helps AI agents or intelligent assistants convert documents between various common formats. You input a document (like a Markdown file, HTML, DOCX, PDF, or plain text) and specify the desired output format, and the tool provides the converted document. It's designed for developers building AI agents that need to process and transform documents for different applications.

AI agent development document processing AI assistant tools large language model integration workflow automation

About mcp_documents_reader

xt765/mcp_documents_reader

Model Context Protocol (MCP) server exposes tools to read multiple document types including DOCX, PDF, Excel, and TXT. This has been tested on Trae Desktop.

This tool helps AI assistants quickly understand information locked away in common document types like Word files, PDFs, Excel spreadsheets, and plain text. You provide the AI with a document file, and it extracts the raw text content for the AI to process. This is ideal for anyone leveraging AI agents for information retrieval, document analysis, or content synthesis from diverse sources.

AI agent productivity document analysis information extraction AI workflow automation digital content processing

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