MCP-RAG system built with the Model Context Protocol (MCP) that handles large files (up to 200MB) using intelligent chunking strategies, multi-format document support, and enterprise-grade reliability.
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"iflow-mcp-anuragb7-mcp-rag","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# 📚 MCP-RAG MCP-RAG system built with the Model Context Protocol (MCP) that handles large files (up to 200MB) using intelligent chunking strategies, multi-format document support, and enterprise-grade reliability. [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://github.com/modelcontextprotocol) ## 🌟 Features ### 📄 **Multi-Format Document Support** - **PDF**: Intelligent page-by-page processing with table detection - **DOCX**: Paragraph and table extraction with formatting preservation - **Excel**: Sheet-aware processing with column context (.xlsx/.xls) - **CSV**: Smart row batching with header preservation - **PPTX**: Support for PPTX - **IMAGE**: Suppport for jpeg , png , webp , gif etc and OCR ### 🚀 **Large File Processing** - **Adaptive chunking**: Different strategies based on file size - **Memory management**: Streaming processing for 50MB+ files - **Progress tracking**: Real-time progress indicators - **Timeout handling**: Graceful handling of long-running operations ### 🧠 **Advanced RAG Capabilities** - **Semantic search**: Vector similarity with confidence scores - **Cross-document queries**: Search across multiple documents simultaneously - **Source attribution**: Citations with similarity scores - **Hybrid retrieval**: Combine semantic and keyword search ### 🔌 **Model Context Protocol (MCP) Integration** - **Universal tool interface**: Standardized AI-to-tool communication - **Auto-discovery**: LangChain agents automatically find and use tools - **Secure communication**: Built-in permission controls - **Extensible architecture**: Easy to add new document processors ### 🏢 **Enterprise Ready** - **Custom LLM endpoints**: Support for any OpenAI-compatible API - **Vector database options**: ChromaDB (local) + …
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.