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Excel Reader

Integrates with Excel files to enable efficient processing and analysis of large spreadsheet datasets through automatic...

Created byApr 23, 2025

MCP Excel Reader

![smithery badge](https://smithery.ai/badge/@ArchimedesCrypto/excel-reader-mcp-chunked) A Model Context Protocol (MCP) server for reading Excel files with automatic chunking and pagination support. Built with SheetJS and TypeScript, this tool helps you handle large Excel files efficiently by automatically breaking them into manageable chunks.
<a href="https://glama.ai/mcp/servers/jr2ggpdk3a"><img width="380" height="200" src="https://glama.ai/mcp/servers/jr2ggpdk3a/badge" alt="Excel Reader MCP server" /></a>

Features

  • Read Excel files (.xlsx, .xls) with automatic size limits
  • Automatic chunking for large datasets
  • Sheet selection and row pagination
  • Proper date handling
  • Optimized for large files
  • Error handling and validation

Installation

Installing via Smithery

To install Excel Reader for Claude Desktop automatically via Smithery:

As an MCP Server

  1. Install globally:
  1. Add to your MCP settings file (usually at ~/.config/claude/settings.json or equivalent):

For Development

  1. Clone the repository:
  1. Install dependencies:
  1. Build the project:

Usage

Usage

The Excel Reader provides a single tool read_excel with the following parameters:

Basic Usage

When used with Claude or another MCP-compatible AI:
The AI will use the tool to read the file, automatically handling chunking for large files.

Features

  1. Automatic Chunking
  1. Sheet Selection
  1. Row Pagination
  1. Error Handling

Extending with SheetJS Features

The Excel Reader is built on SheetJS and can be extended with its powerful features:

Available Extensions

  1. Formula Handling
  1. Cell Formatting
  1. Data Validation
  1. Sheet Features
For more features and detailed documentation, visit the SheetJS Documentation.

Contributing

  1. Fork the repository
  1. Create your feature branch (git checkout -b feature/amazing-feature)

MCP Excel Reader

![smithery badge](https://smithery.ai/badge/@ArchimedesCrypto/excel-reader-mcp-chunked) A Model Context Protocol (MCP) server for reading Excel files with automatic chunking and pagination support. Built with SheetJS and TypeScript, this tool helps you handle large Excel files efficiently by automatically breaking them into manageable chunks.
<a href="https://glama.ai/mcp/servers/jr2ggpdk3a"><img width="380" height="200" src="https://glama.ai/mcp/servers/jr2ggpdk3a/badge" alt="Excel Reader MCP server" /></a>

Features

  • Read Excel files (.xlsx, .xls) with automatic size limits
  • Automatic chunking for large datasets
  • Sheet selection and row pagination
  • Proper date handling
  • Optimized for large files
  • Error handling and validation

Installation

Installing via Smithery

To install Excel Reader for Claude Desktop automatically via Smithery:

As an MCP Server

  1. Install globally:
  1. Add to your MCP settings file (usually at ~/.config/claude/settings.json or equivalent):

For Development

  1. Clone the repository:
  1. Install dependencies:
  1. Build the project:

Usage

Usage

The Excel Reader provides a single tool read_excel with the following parameters:

Basic Usage

When used with Claude or another MCP-compatible AI:
The AI will use the tool to read the file, automatically handling chunking for large files.

Features

  1. Automatic Chunking
  1. Sheet Selection
  1. Row Pagination
  1. Error Handling

Extending with SheetJS Features

The Excel Reader is built on SheetJS and can be extended with its powerful features:

Available Extensions

  1. Formula Handling
  1. Cell Formatting
  1. Data Validation
  1. Sheet Features
For more features and detailed documentation, visit the SheetJS Documentation.

Contributing

  1. Fork the repository
  1. Create your feature branch (git checkout -b feature/amazing-feature)
  1. Commit your changes (git commit -m 'Add some amazing feature')
  1. Push to the branch (git push origin feature/amazing-feature)
  1. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

MCP Excel Reader

![smithery badge](https://smithery.ai/badge/@ArchimedesCrypto/excel-reader-mcp-chunked) A Model Context Protocol (MCP) server for reading Excel files with automatic chunking and pagination support. Built with SheetJS and TypeScript, this tool helps you handle large Excel files efficiently by automatically breaking them into manageable chunks.
<a href="https://glama.ai/mcp/servers/jr2ggpdk3a"><img width="380" height="200" src="https://glama.ai/mcp/servers/jr2ggpdk3a/badge" alt="Excel Reader MCP server" /></a>

Features

  • Read Excel files (.xlsx, .xls) with automatic size limits
  • Automatic chunking for large datasets
  • Sheet selection and row pagination
  • Proper date handling
  • Optimized for large files
  • Error handling and validation

Installation

Installing via Smithery

To install Excel Reader for Claude Desktop automatically via Smithery:

As an MCP Server

  1. Install globally:
  1. Add to your MCP settings file (usually at ~/.config/claude/settings.json or equivalent):

For Development

  1. Clone the repository:
  1. Install dependencies:
  1. Build the project:

Usage

Usage

The Excel Reader provides a single tool read_excel with the following parameters:

Basic Usage

When used with Claude or another MCP-compatible AI:
The AI will use the tool to read the file, automatically handling chunking for large files.

Features

  1. Automatic Chunking
  1. Sheet Selection
  1. Row Pagination
  1. Error Handling

Extending with SheetJS Features

The Excel Reader is built on SheetJS and can be extended with its powerful features:

Available Extensions

  1. Formula Handling
  1. Cell Formatting
  1. Data Validation
  1. Sheet Features
For more features and detailed documentation, visit the SheetJS Documentation.

Contributing

  1. Fork the repository
  1. Create your feature branch (git checkout -b feature/amazing-feature)
  1. Commit your changes (git commit -m 'Add some amazing feature')
  1. Push to the branch (git push origin feature/amazing-feature)
  1. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments