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cyberchitta/llm-context.py

by Various

Share code with LLMs via Model Context Protocol or clipboard. Rule-based customization enables easy switching between different tasks (like code review and documentation). Includes

C

MCP

cyberchitta/llm-context.py

Added 1 June 2026

#claude-desktop #cli #coding #model-context-protocol #tools

Overview

Shares code with large language models via the Model Context Protocol or clipboard. Rule-based customization lets you switch between tasks like code review and documentation. Includes smart code outlining to improve context clarity.

Best for

Best for
Developers who regularly share code with LLMs for review, documentation, or refactoring tasks.

Use cases

  • Send code snippets to an LLM for review without manual copy-paste
  • Automatically tailor code context for different documentation requests
  • Generate structured code outlines for LLM-based refactoring

Notes

Shares code with large language models via the Model Context Protocol or clipboard. Rule-based customization lets you switch between tasks like code review and documentation. Includes smart code outlining to improve context clarity.

301 stars on GitHub. Last updated 2026-05-27. Licensed Apache-2.0.

Use cases

  • Send code snippets to an LLM for review without manual copy-paste
  • Automatically tailor code context for different documentation requests
  • Generate structured code outlines for LLM-based refactoring

Pros

  • Supports multiple sharing methods (MCP and clipboard)
  • Rule system makes task switching easy and repeatable
  • Smart outlining reduces noise in the context sent to the LLM

Cons

  • Requires Python runtime to use
  • Rule configuration has a learning curve for new users
  • Limited to code context sharing, not general file or data sharing

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

Pros

  • Supports multiple sharing methods (MCP and clipboard)
  • Rule system makes task switching easy and repeatable
  • Smart outlining reduces noise in the context sent to the LLM

Cons

  • Requires Python runtime to use
  • Rule configuration has a learning curve for new users
  • Limited to code context sharing, not general file or data sharing