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juergenkoller-software/distill-mcp

by Various

Open-source MCP server bridge for Distill — let Claude/Cursor rename your files based on content (AI: Claude/OpenAI/Gemini/Ollama/Apple).

J

MCP

juergenkoller-software/distill-mcp

Added 11 June 2026

#ai #automation #claude #claude-code #claude-desktop #cursor #file-rename #json-rpc

Overview

An open-source MCP server bridge for Distill that enables Claude or Cursor to rename files based on their content. It supports multiple AI models including Claude, OpenAI, Gemini, Ollama, and Apple. The tool is written in Swift and available on GitHub.

Best for

Best for
Developers who want AI-assisted file renaming within Cursor or Claude environments

Use cases

  • Renaming files by content analysis using AI models
  • Integrating AI file renaming into Cursor or Claude workflows
  • Automating file organization based on semantic content

Notes

An open-source MCP server bridge for Distill that enables Claude or Cursor to rename files based on their content. It supports multiple AI models including Claude, OpenAI, Gemini, Ollama, and Apple. The tool is written in Swift and available on GitHub.

0 stars on GitHub. Last updated 2026-06-08. Licensed MIT.

Use cases

  • Renaming files by content analysis using AI models
  • Integrating AI file renaming into Cursor or Claude workflows
  • Automating file organization based on semantic content

Pros

  • Supports multiple AI providers (Claude, OpenAI, Gemini, Ollama, Apple)
  • Open-source and customizable
  • Integrates directly into Cursor and Claude

Cons

  • Zero stars on GitHub indicates limited community adoption
  • Limited documentation or support due to low visibility
  • Requires setup of an MCP server and API keys for AI models

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

Pros

  • Supports multiple AI providers (Claude, OpenAI, Gemini, Ollama, Apple)
  • Open-source and customizable
  • Integrates directly into Cursor and Claude

Cons

  • Zero stars on GitHub indicates limited community adoption
  • Limited documentation or support due to low visibility
  • Requires setup of an MCP server and API keys for AI models