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io.github.ankitpal181/toon-parse-mcp

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

Open source Open in the app JSON README (API)

About

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

Details

Kind
MCP servers
Topic
AI, RAG & memory
Publisher
ankitpal181
Origin
official
Category
ferramentas
Transport
local
Version
1.0.3-beta
Last push
2026-01-17T13:35:49Z
Repository state
ativo
Language
Python
License
MIT
Added
2026-08-29 03:02:25
Updated
2026-08-29 03:02:25
Origin id
io.github.ankitpal181/toon-parse-mcp

README

# toon-parse MCP Server

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

[![MCP Registry](https://img.shields.io/badge/MCP-Registry-blue)](https://registry.modelcontextprotocol.io/)
[![PyPI version](https://badge.fury.io/py/toon-parse-mcp.svg)](https://badge.fury.io/py/toon-parse-mcp)

A specialized [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 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

```bash
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:

```json
{
  "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:

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

### Claude Desktop

Add this to your `claude_desktop_config.json`:

```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:
   ```bash
   pip install -e ".[test]"
   ```
2. Run tests:
   ```bash
   pytest tests/
   ```

## Requirements

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

## License

MIT License - see [LICENSE](LICENSE) for details.

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