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Provides a token-efficient MCP server that converts data to TOON and optimizes code context for AI agents.
Configuration
View docs{
"mcpServers": {
"ankitpal181-toon-parse-mcp": {
"command": "python3",
"args": [
"-m",
"toon_parse_mcp.server"
]
}
}
}You can deploy the toon-parse MCP Server to optimize token usage by converting data to the compact TOON format and removing non-essential context from code files, making AI agents work more efficiently with code and data inputs.
Start the toon-parse MCP Server and connect it from your MCP client. Once active, your client gains access to two tools that optimize data and code contexts: optimize_input_context for transforming raw inputs (JSON, XML, YAML, CSV) into TOON, and read_and_optimize_file for condensing local code files by removing inline comments and unnecessary whitespace while preserving useful structure. You can also rely on the built-in efficiency protocol to guide token-saving behavior.
Prerequisites before installation include having Python 3.10 or newer installed on your system. The server relies on Python packages and standard MCP tooling.
Install the toon-parse MCP Server package using Python's package manager:
pip install toon-parse-mcpTo run the server locally, start the module using Python so it serves as an MCP endpoint that your clients can connect to.
Typical runtime command to start the server:
python3 -m toon_parse_mcp.serverConfigure your MCP clients to recognize toon-parse as a server. Use the following example configurations in the client’s MCP settings to register toon-parse-mcp as an active MCP server.
{
"mcpServers": {
"toon-parse-mcp": {
"command": "python3",
"args": ["-m", "toon_parse_mcp.server"]
}
}
}Processes raw inputs written in JSON, XML, YAML, or CSV and returns a token-optimized TOON representation.
Reads a local code file and returns a token-optimized version by removing inline comments and minimizing whitespace while preserving functionality.