"excel" matching MCP servers:

This read-only MCP Server allows you to connect to Microsoft Excel data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for Microsoft Excel (https://www.cdata.com/drivers/excel/download/mcp).

Enables SQL querying over CSV and Excel files using DuckDB, providing tools to load files, inspect schemas, and run read-only queries via MCP.

A lightweight document parser MCP server that enables Claude to parse PDFs, Word, Excel, images (OCR), and other document formats with support for chunking and metadata extraction.

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.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Offline AI-powered local file search MCP server for Windows. Searches inside document contents (Word, Excel, PDF, PowerPoint, HWP) using BM25 + dense vector hybrid search. 100% local, no cloud, no login, no telemetry.

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.

MCP server for indexing, semantic search, and generation of multi-format documents. Exposes 13 tools over JSON-RPC 2.0 so an LLM can search your local PDF, Excel, and Word files, and create or edit Excel and Word documents.

Enables AI agents to inspect and convert PDF, PowerPoint, Excel, and many other file formats into clean, structured Markdown, with chunking support for long documents.

Turns Excel/CSV folders into a traceable, read-only AI knowledge layer; provides deterministic data queries, schema discovery, and evidence-backed search via MCP.

Enables AI agents to extract text and structure from PDF, Word, and Excel documents as LLM-friendly Markdown or JSON, with tools for parsing files and listing supported formats.

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.

Enables AI to efficiently analyze and extract structured data from complex documents, especially Excel files, by providing tools like section reading and field mapping to reduce token usage and improve success rates.

Enables MCP clients to run three-stage drone logistics site selection, load CSV/Excel data, retrieve domain knowledge, reverse-geocode coordinates, and receive progress heartbeats for long-running tasks over stdio JSON-RPC.

Enables AI assistants to parse and search documents including PDF, Word, Excel, PowerPoint, and images via OCR, with support for semantic search and batch processing.

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.

An AI-powered office assistant using LLM, RAG, and MCP tools to answer weather queries, retrieve information from documents, and automatically generate Excel and Word reports.

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.

Retrieval-augmented MCP server for company project documents (Markdown, Word, Excel, PDF) with vector search, offline embedding, and token-based project authorization.

Converts Excel table definitions to structured JSON and exposes them to LLMs via MCP tools like list_tables and get_table_schema, enabling accurate SQL generation and data modeling assistance.

On-premises knowledge retrieval over the Markdown, text, PDF, Word and Excel files an organization keeps, with the rules you set enforced before anything is retrieved. Agents connect over MCP, get cited passages, and read from it and never write to it.

Enables natural-language querying of large Excel, Access, and Parquet datasets, returning executed SQL with results for verification. Exposes tools for status checking, table listing, and asking questions through MCP.

Local MCP server that lets agents read PDF, DOCX, PPT, Excel, HTML, EPUB, email and 40+ other formats as Markdown. Read PDF, Office, HTML, EPUB, email and 40+ formats as Markdown; outline, search and diff documents inside allowlisted folders. Writing documents from Markdown and editing DOCX, PPTX, HTML and Markdown files in place are opt-in. All file access is confined to allowlisted folders.

Local-first agent memory & knowledge service (v2.4.4): hybrid retrieval (vector + BM25) + offline CSV/Excel row-level Q&A without LLM; REST + MCP dual protocol (13 tools incl. list/delete documents); memory governance; Python.

MCP server for tabular data retrieval that indexes local CSV, Excel, Parquet, and JSONL files once and answers questions via column profiles, filtered rows, server-side aggregations, and joins, drastically reducing token usage for large datasets.

Local search over your code and documents for AI agents. Hybrid search and a code graph over your code and documents (PDF, Word, PowerPoint, Excel, EPUB), served over MCP with file and line spans, from one Rust binary on your machine.

Converts PDF, Word, PowerPoint, Excel, and HTML documents into Markdown, handling files up to 8 MB in a single call and routing larger or scanned files through an OCR and table-detection queue that returns Markdown plus a JSON structure file. Results arrive either by polling or a signed webhook.

This MCP server enables employees to ask IT questions, receive evidence-backed answers, and trigger traceable work orders or escalations through authenticated MCP tools. It also supports status checks, durable action jobs, and auditable work-order, Excel ledger, and escalation workflows.

Imagine you could hand Claude a filing cabinet of your documents and say "remember all of this." Later you just ask questions, and Claude answers from what it remembers — citing which document each fact came from. That's Memorised them All. It's a small add-on (an MCP server) for Claude Desktop and Claude Code that: Reads your files — PDFs, Word/Excel/PowerPoint, web pages, images (with OCR), ev

Enables Claude to search and read the user's local document collection (PDF, Word, Excel, Markdown, plain text) during a conversation by subject, by file name/path, or by exact word, and to retrieve file contents page by page. It also supports finding folders relevant to a topic and checking indexing status, with everything running locally on the user's machine.

Enables agents to run full-text search inside Word, PowerPoint, Excel, PDF, and plain-text files by extracting their contents into a single local SQLite FTS5 index. Exposes tools to search documents with ranked highlighted snippets and human-readable locators, list indexed files, and incrementally build or refresh the index.

This server lets users submit natural-language requirements, screenshots, or Excel files and receive grounded asset-matching recommendations, with an agentic loop that judges sufficiency, iteratively corrects its own search, and explicitly reports when no asset can support a request. It supports interactive feedback, multi-turn refinement, and integration through Web, REST, and MCP.

Enables AI agents to convert PDF, Word, PowerPoint, Excel, HTML, EPUB, Outlook messages and web pages into Markdown for LLM and RAG workflows. Runs over stdio or HTTP and reproduces Microsoft markitdown's output byte for byte without a Python environment.