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PSU3D0/spreadsheet-mcp

by Various

MCP server for spreadsheet analysis and editing. Slim, token-efficient tool surface designed for LLM agents.

P

MCP

PSU3D0/spreadsheet-mcp

Added 1 June 2026

#spreadsheet #spreadsheet-agent #spreadsheet-ai #spreadsheet-mcp

Overview

An MCP server for spreadsheet analysis and editing, built in Rust. It exposes a slim, token-efficient tool surface designed for LLM agents to read and modify spreadsheet data.

Best for

Best for
Developers building LLM agents that need lightweight spreadsheet read/write capabilities

Use cases

  • Automating spreadsheet data extraction and formatting via LLM agents
  • Integrating spreadsheet editing into AI-driven workflows
  • Performing lightweight spreadsheet operations without a full office suite

How to use

Install

npm i -g agent-spreadsheet

Tools exposed

  • spreadsheet-kit
  • spreadsheet-mcp
  • spreadsheet-kit-wasm
  • agent-spreadsheet
  • spreadsheet-kit-sdk

Tested with

Claude Desktop, Claude Code, Continue

Notes

An MCP server for spreadsheet analysis and editing, built in Rust. It exposes a slim, token-efficient tool surface designed for LLM agents to read and modify spreadsheet data.

46 stars on GitHub. Last updated 2026-05-13. Licensed Apache-2.0.

Use cases

  • Automating spreadsheet data extraction and formatting via LLM agents
  • Integrating spreadsheet editing into AI-driven workflows
  • Performing lightweight spreadsheet operations without a full office suite

Pros

  • Rust implementation offers fast performance and low resource usage
  • Token-efficient design reduces LLM API costs
  • Focused tool surface simplifies integration for agents

Cons

  • Small community (46 stars) may mean limited support and documentation
  • Narrow scope may not suit complex spreadsheet tasks
  • Requires MCP-compatible LLM infrastructure to use

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Rust implementation offers fast performance and low resource usage
  • Token-efficient design reduces LLM API costs
  • Focused tool surface simplifies integration for agents

Cons

  • Small community (46 stars) may mean limited support and documentation
  • Narrow scope may not suit complex spreadsheet tasks
  • Requires MCP-compatible LLM infrastructure to use
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