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JSON2TOON MCP Server

JSON2TOON MCP Server

by muhammedehab35
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_optimal_strategy

Analyzes provided JSON to determine the most efficient compression approach using AI pattern detection.

Instructions

Get AI-recommended optimal compression strategy

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "json_data": "<json_data>"
    },
    "name": "get_optimal_strategy"
  }
}

Input Schema

NameRequiredDescriptionDefault
json_dataYesJSON data to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavior. It only states the purpose, omitting details like side effects, performance, or return format. This is insufficient for an AI agent to understand what happens when invoked.

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

Conciseness4/5

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

The description is a single concise sentence that earns its place. However, it could be slightly more structured to include additional context without bloat.

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?

The description is too minimal for a tool that returns a recommendation. There is no output schema, and no explanation of what the strategy looks like or any limitations. Context around its use among siblings 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% (one parameter with a clear description 'JSON data to analyze'). The tool description adds no extra parameter information, but the baseline score of 3 applies because the schema already covers the parameter meaning.

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 gets an AI-recommended optimal compression strategy, which is a specific verb-resource pair. However, it does not differentiate from siblings like smart_optimize or estimate_savings, which could overlap.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context, leaving the agent without decision support.

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

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