"excel" matching MCP servers:

Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.

Upload documents (Word, Excel, PDF, PowerPoint) to a vector RAG store and perform semantic search with page-level citations. Queries are free; ingestion costs credits at break-even pricing.

Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.

RAG-powered document search server that enables semantic search across large collections of legal and business documents (PDF, Word, Excel, PowerPoint) using local embeddings with no API costs.

Enables semantic search and question-answering over semiconductor component data stored in Excel files using ChromaDB vector database and HuggingFace language models through a RAG pipeline.

Transforms CSV and Excel data into Markdown-formatted vector embeddings stored in a local ChromaDB instance for semantic search. It enables MCP clients to retrieve relevant tabular data through single-row, batch, or free-text queries.