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"How to Read an Excel File" matching MCP servers:

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    Enables AI agents to read Excel files with full content extraction, including embedded images, cell data, formulas, and merged cells, returning multimodal content for vision-capable models.
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    MIT
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    Enables conversational data analysis of Excel/CSV files through natural language queries, powered by 395 Excel functions via HyperFormula and multi-provider AI. Supports advanced analytics, bulk operations, financial modeling, and large file processing with intelligent chunking.
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    MIT
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    Reads Excel files and returns each sheet's content as CSV text. Enables AI models to extract data from Excel spreadsheets via natural language.
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    MIT
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    Enables read-only SQL querying and exploration of data files (CSV, Parquet, JSON, Excel, etc.) via DuckDB, supporting local paths, globs, URLs, and S3 buckets.
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    MIT
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    Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.
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    MIT
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    An MCP server for searching and downloading books from Library Genesis, supporting EPUB, MOBI, PDF, and more through natural language queries.
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    MIT
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    MCP server for read-only access to Charles Schwab market data and account information. It provides quotes, price history, technical indicators, fundamentals, option chains, market hours, movers, and account/transaction data via the official Schwab API.
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    Read-only MCP tools for authenticated Open Science Framework projects, components, files, and contributors.
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    Apache 2.0
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    An MCP server providing access to XRootD file systems, allowing LLMs to browse directories, read file metadata, and access contents via the root:// protocol. It supports advanced features like campaign discovery, file searching, and ROOT file analysis for scientific data management.
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    MIT
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    MCP server for reading and inspecting local Excel files (.xlsx, .xlsm, .xls, .xlsb, .ods) with tools for inspecting metadata, reading ranges, and profiling structure.
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    MIT
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    Webhound is a research engine with an explicit effort control: a prompt defines the investigation and a dollar budget defines how much work it deserves. It returns cited reports or sourced datasets with the working documents, claims, sources, limitations, and evidence pack behind the answer.
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    MIT