toon-parse-mcp

@ankitpal181/toon-parse-mcp

0 Stars 7 次浏览 ankitpal181 更新于 2026-08-23

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

MCP 服务配置

复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用

{
  "mcpServers": {
    "toon-parse-mcp": {
      "args": [
        "toon-parse-mcp@1.0.4-beta"
      ],
      "command": "uvx"
    }
  }
}

可用工具（5 个）

该服务在 MCP 协议中暴露的工具，AI 可按需调用

tavily_search 14 个参数需填 1 项 Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.

必填参数：query

tavily_extract 6 个参数需填 1 项 Extract content from URLs. Returns raw page content in markdown or text format.

Extract content from URLs. Returns raw page content in markdown or text format.

必填参数：urls

tavily_crawl 11 个参数需填 1 项 Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.

必填参数：url

tavily_map 8 个参数需填 1 项 Map a website's structure. Returns a list of URLs found starting from the base URL.

Map a website's structure. Returns a list of URLs found starting from the base URL.

必填参数：url

tavily_research 2 个参数需填 1 项 Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.

必填参数：input

服务介绍

toon-parse MCP Server

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

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:

Optimizing Code Context: Stripping comments and redundant spacing from code files while preserving functional structure and docstrings.
Data Format Conversion: Converting JSON, XML, YAML, and CSV inputs into the compact TOON format to save tokens.
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

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

Windsurf

Click the hammer icon in the Cascade toolbar and select "Configure".
Alternatively, edit ~/.codeium/windsurf/mcp_config.json directly.
Add the following to the mcpServers object:

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

Antigravity

Open the MCP store via the "..." menu at the top right of the agent panel.
Select "Manage MCP Servers" -> "View raw config".
Alternatively, edit ~/.gemini/antigravity/mcp_config.json directly.
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:

Install test dependencies:
```
pip install -e ".[test]"
```
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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