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tumf/mcp-text-editor

by Various

🐍 🏠 - A line-oriented text file editor. Optimized for LLM tools with efficient partial file access to minimize token usage.

T

MCP

tumf/mcp-text-editor

Added 1 June 2026

Overview

A line-oriented text file editor built as an MCP server for LLM tool use. It provides efficient partial file access to minimize token consumption, reading and writing only the lines needed rather than whole files.

Best for

Best for
Developers building LLM-based coding assistants that need low-cost file editing

Use cases

  • Edit specific lines in large files without loading the entire document
  • Apply targeted text changes under LLM orchestration
  • Read file snippets for context-aware code or content modifications

How to use

Install

uvx mcp-text-editor

Tools exposed

  • uvx
  • uv
  • pip
  • pyenv

Tested with

Claude Desktop

Example client config

{\n  "mcpServers": {\n    "text-editor": {\n      "command": "uvx",\n      "args": [\n        "mcp-text-editor"\n      ]\n    }\n  }\n}

Notes

A line-oriented text file editor built as an MCP server for LLM tool use. It provides efficient partial file access to minimize token consumption, reading and writing only the lines needed rather than whole files.

190 stars on GitHub. Last updated 2026-03-17. Licensed MIT.

Use cases

  • Edit specific lines in large files without loading the entire document
  • Apply targeted text changes under LLM orchestration
  • Read file snippets for context-aware code or content modifications

Pros

  • Token-efficient design reduces cost and latency for LLM interactions
  • Simple line-oriented operations align well with LLM input/output patterns
  • Open-source Python codebase easy to audit and extend

Cons

  • Line-oriented approach not ideal for multiline or structural edits
  • Small community with 190 stars may limit support and updates
  • Requires setting up an MCP server environment for use

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

Pros

  • Token-efficient design reduces cost and latency for LLM interactions
  • Simple line-oriented operations align well with LLM input/output patterns
  • Open-source Python codebase easy to audit and extend

Cons

  • Line-oriented approach not ideal for multiline or structural edits
  • Small community with 190 stars may limit support and updates
  • Requires setting up an MCP server environment for use
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