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Toon Parse MCP Server

by Ankitpal181
Developer ToolsLow Risk10.0MCP RegistryLocal
Free

Server data from the Official MCP Registry

MCP server that reduces LLM context by removing code comments and converting data formats to TOON

About

MCP server that reduces LLM context by removing code comments and converting data formats to TOON

Security Report

10.0
Low Risk10.0Low Risk

Valid MCP server (2 strong, 3 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

7 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

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env_vars

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Shell Command Execution

Runs commands on your machine. Be cautious — only use if you trust this plugin.

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-ankitpal181-toon-parse-mcp": {
      "args": [
        "toon-parse-mcp"
      ],
      "command": "uvx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

toon-parse MCP Server

mcp-name: io.github.ankitpal181/toon-parse-mcp

MCP Registry PyPI version

A specialized Model Context Protocol (MCP) server that optimizes token usage by converting data to TOON (Token-Oriented Object Notation) and stripping non-essential context from code files.

Overview

The toon-parse-mcp MCP server helps AI agents (like Cursor, Claude Desktop, etc.) operate more efficiently by:

  1. Optimizing Code Context: Stripping comments and redundant spacing from code files while preserving functional structure and docstrings.
  2. Data Format Conversion: Converting JSON, XML, YAML, and CSV inputs into the compact TOON format to save tokens.
  3. Mandatory Efficiency Protocol: A built-in resource that instructs LLMs to prioritize token-saving tools.

Features

Tools

  • optimize_input_context(raw_input: str): Processes raw text data (JSON/XML/CSV/YAML) and returns optimized TOON format.
  • read_and_optimize_file(file_path: str): Reads a local code file and returns a token-optimized version (no inline comments, minimized whitespace).

Resources

  • protocol://mandatory-efficiency: Provides a strict system instruction prompt for LLMs to ensure they use the optimization tools correctly.

Installation

pip install toon-parse-mcp

Configuration

Cursor

  1. Open Cursor Settings -> MCP.
  2. Click "+ Add New MCP Server".
  3. Name: toon-parse-mcp
  4. Type: command
  5. Command: python3 -m toon_parse_mcp.server (Ensure your environment is active or use absolute path to python)

Windsurf

  1. Click the hammer icon in the Cascade toolbar and select "Configure".
  2. Alternatively, edit ~/.codeium/windsurf/mcp_config.json directly.
  3. Add the following to the mcpServers object:
{
  "mcpServers": {
    "toon-parse-mcp": {
      "command": "python3",
      "args": ["-m", "toon_parse_mcp.server"]
    }
  }
}

Antigravity

  1. Open the MCP store via the "..." menu at the top right of the agent panel.
  2. Select "Manage MCP Servers" -> "View raw config".
  3. Alternatively, edit ~/.gemini/antigravity/mcp_config.json directly.
  4. Add the following to the mcpServers object:
{
  "mcpServers": {
    "toon-parse-mcp": {
      "command": "python3",
      "args": ["-m", "toon_parse_mcp.server"]
    }
  }
}

Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "toon-parse-mcp": {
      "command": "python3",
      "args": ["-m", "toon_parse_mcp.server"]
    }
  }
}

Usage

When the server is active, the AI will have access to the optimize_input_context and read_and_optimize_file tools. You can also refer to the efficiency protocol by asking the AI to "check the mandatory efficiency protocol".

Testing

To run the test suite:

  1. Install test dependencies:
    pip install -e ".[test]"
    
  2. Run tests:
    pytest tests/
    

Requirements

  • Python >= 3.10
  • mcp >= 1.25.0
  • toon-parse >= 2.4.3

License

MIT License - see LICENSE for details.

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Links

Source CodePyPI Package

Details

Published February 24, 2026
Version 1.0.3-beta
0 installs
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