How do I use JSON to TOON MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@JSON to TOON MCP Server convert this JSON to TOON: {"users": [{"id": 1, "name": "Alice"}]}" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

JSON to TOON MCP Server

by mhabedini

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

JSON to TOON MCP Server

A Model Context Protocol (MCP) server that provides tools for converting JSON data to TOON (Token-Oriented Object Notation) format. TOON is a token-efficient format designed specifically for LLM applications, reducing token usage by 30-60% compared to JSON.

Features

JSON to TOON Conversion: Convert JSON data to TOON format with customizable options
TOON to JSON Conversion: Convert TOON format back to JSON
Token Savings Analysis: Analyze potential token savings before conversion
MCP Protocol Support: Full MCP server implementation for integration with LLM applications

Related MCP server: TOON MCP Server

Quick Start

1. Install the Package

npm install -g json-to-toon-mcp-server

2. Configure Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "json-to-toon": {
      "command": "npx",
      "args": ["json-to-toon-mcp-server"]
    }
  }
}

3. Restart Claude Desktop

Restart Claude Desktop and the MCP server will be available.

Installation

npm install

Usage

Running the Server

npm start

The server runs on stdio and can be connected to any MCP client.

Available Tools

1. `convert_json_to_toon`

Converts JSON data to TOON format.

Parameters:

json_data (string, required): JSON data to convert
options (object, optional): Conversion options
- delimiter (string): Delimiter for tabular arrays (default: ",")
- indentation (string): Indentation string (default: " ")
- show_length_markers (boolean): Show length markers [N] for arrays (default: true)

Example:

{
  "json_data": "{\"users\": [{\"id\": 1, \"name\": \"Alice\"}]}",
  "options": {
    "delimiter": ",",
    "show_length_markers": true
  }
}

2. `convert_toon_to_json`

Converts TOON format back to JSON.

Parameters:

toon_data (string, required): TOON data to convert

Example:

{
  "toon_data": "users[1]{id,name}:\n  1,Alice"
}

3. `analyze_token_savings`

Analyzes potential token savings when converting JSON to TOON.

Parameters:

json_data (string, required): JSON data to analyze

Example:

{
  "json_data": "{\"users\": [{\"id\": 1, \"name\": \"Alice\"}]}}"
}

Example Conversions

JSON to TOON

JSON Input:

{
  "users": [
    { "id": 1, "name": "Alice", "role": "admin" },
    { "id": 2, "name": "Bob", "role": "user" }
  ]
}

TOON Output:

users[2]{id,name,role}:
  1,Alice,admin
  2,Bob,user

Token Savings

The TOON format typically reduces token usage by 30-60% compared to JSON, making it ideal for LLM applications where token efficiency is important.

Development

Running Tests

npm test

Development Mode

npm run dev

Integration with MCP Clients

This server can be integrated with any MCP-compatible client. The server communicates via stdio, making it suitable for integration with various LLM applications and development tools.

Manual Configuration for Claude Desktop

To add this MCP server to Claude Desktop manually, add the following configuration to your claude_desktop_config.json file.

Config file locations:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/Claude/claude_desktop_config.json

To add this MCP server to Claude Desktop manually, add the following configuration to your claude_desktop_config.json:

{
  "mcpServers": {
    "json-to-toon": {
      "command": "npx",
      "args": ["json-to-toon-mcp-server"],
      "env": {}
    }
  }
}

Alternative configuration using local installation:

{
  "mcpServers": {
    "json-to-toon": {
      "command": "node",
      "args": ["/path/to/json-to-toon-mcp-server/index.js"],
      "env": {}
    }
  }
}

Configuration for development (from source):

{
  "mcpServers": {
    "json-to-toon": {
      "command": "node",
      "args": ["index.js"],
      "cwd": "/path/to/json-to-toon-mcp-server"
    }
  }
}

Configuration Notes

Replace /path/to/json-to-toon-mcp-server with the actual path to your installation
The npx method requires the package to be installed globally (npm install -g json-to-toon-mcp-server)
The server communicates via stdio, so no additional network configuration is needed
After adding the configuration, restart Claude Desktop for changes to take effect

TOON Format Benefits

Token Efficient: 30-60% fewer tokens than JSON
LLM-Friendly: Structured format that's easy for LLMs to parse
Lossless: Full bidirectional conversion between JSON and TOON
Human Readable: Maintains readability while being compact
Schema Aware: Explicit structure declarations help with validation

License

MIT

Available Tools

3 tools

analyze_token_savingsC

Analyze potential token savings when converting JSON to TOON

ParametersJSON Schema

Name	Required	Description	Default
`json_data`	Yes	JSON data to analyze for token savings

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes 'potential token savings,' implying a read-only, non-destructive operation, but doesn't clarify if it requires specific permissions, how it calculates savings, or what the output format is. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence: 'Analyze potential token savings when converting JSON to TOON.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'token savings' means, how the analysis is performed, or what the result looks like (e.g., a percentage, comparison). For a tool with no structured output and minimal behavioral context, more detail is needed to be fully helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with one parameter 'json_data' documented as 'JSON data to analyze for token savings.' The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score is 3, as the schema adequately handles parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Analyze potential token savings when converting JSON to TOON.' It specifies the action (analyze), the resource (token savings), and the context (conversion from JSON to TOON). However, it doesn't explicitly distinguish this analysis tool from its sibling conversion tools (convert_json_to_toon, convert_toon_to_json), which would require a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tools (convert_json_to_toon, convert_toon_to_json) or explain that this is for analysis before conversion, nor does it specify prerequisites or exclusions. The context is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

convert_json_to_toonB

Convert JSON data to TOON (Token-Oriented Object Notation) format for efficient LLM token usage

ParametersJSON Schema

Name	Required	Description	Default
`json_data`	Yes	JSON data to convert to TOON format
`options`	No	Conversion options

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the conversion purpose without behavioral details. It doesn't disclose potential side effects, error conditions, performance characteristics, or what the output looks like (though no output schema exists). For a transformation tool with zero annotation coverage, this is inadequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a transformation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what TOON format is, what the output looks like, or provide any behavioral context. The 100% schema coverage helps with parameters, but overall context for proper tool selection and usage is lacking.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing complete parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It mentions 'JSON data' generally but doesn't elaborate on format requirements or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Convert') and resource ('JSON data to TOON format'), specifying the purpose as transformation for efficiency in LLM token usage. It distinguishes from sibling 'convert_toon_to_json' by direction, but doesn't explicitly contrast with 'analyze_token_savings' beyond implied functional difference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('for efficient LLM token usage') but doesn't provide explicit guidance on when to choose this tool over alternatives like 'analyze_token_savings' or when not to use it. The purpose suggests it's for conversion to TOON format specifically, but lacks comparative context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

convert_toon_to_jsonB

Convert TOON format back to JSON data

ParametersJSON Schema

Name	Required	Description	Default
`toon_data`	Yes	TOON data to convert to JSON format

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the conversion action without disclosing behavioral traits like error handling, performance characteristics, or what happens with malformed input. It doesn't add meaningful context beyond the basic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste—every word contributes directly to explaining the tool's function. It's appropriately sized and front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete for a conversion tool. It doesn't explain what the JSON output looks like, potential errors, or any side effects, leaving significant gaps in understanding the tool's behavior and results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single parameter 'toon_data'. The description adds no additional meaning about parameter usage, format expectations, or examples beyond what the schema provides, meeting the baseline for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('convert') and resource ('TOON format to JSON data'), making the purpose understandable. It distinguishes from sibling 'convert_json_to_toon' by specifying the opposite direction, though it doesn't explicitly name the sibling for comparison.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when TOON data needs conversion to JSON, but provides no explicit guidance on when to use this versus alternatives like 'analyze_token_savings' or prerequisites. The context is clear but lacks specific when/when-not instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

A3.5/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: analyze_token_savings is for analysis, convert_json_to_toon is for one-way conversion, and convert_toon_to_json is for reverse conversion. There is no overlap or ambiguity between these functions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_token_savings, convert_json_to_toon, convert_toon_to_json) with clear, descriptive terms. There are no deviations in naming style.

Tool Count4/5

Three tools are well-scoped for a JSON/TOON conversion server, covering analysis and bidirectional conversion. It feels slightly minimal but reasonable, as core operations are present without bloat.

Completeness5/5

The tool set provides complete coverage for the domain: analysis of token savings, conversion from JSON to TOON, and conversion back from TOON to JSON. There are no obvious gaps for the stated purpose of efficient LLM token usage.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

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JSON to TOON MCP Server

Features

Quick Start

1. Install the Package

2. Configure Claude Desktop

3. Restart Claude Desktop

Installation

Usage

Running the Server

Available Tools

1. convert_json_to_toon

2. convert_toon_to_json

3. analyze_token_savings

Example Conversions

JSON to TOON

Token Savings

Development

Running Tests

Development Mode

Integration with MCP Clients

Manual Configuration for Claude Desktop

Configuration Notes

TOON Format Benefits

License

Available Tools

TDQS

TDQS

TDQS

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

TOON MCP Server

TOON MCP Server

TOONer

ASON MCP Serverofficial

Latest Blog Posts

MCP directory API

1. `convert_json_to_toon`

2. `convert_toon_to_json`

3. `analyze_token_savings`