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JSON2TOON MCP Server

JSON2TOON MCP Server

by muhammedehab35
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

calculate_metrics

Computes compression metrics and savings for JSON data, showing reduction ratios and token counts.

Instructions

Calculate detailed compression metrics and savings

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": "calculate_metrics"
  }
}

Input Schema

NameRequiredDescriptionDefault
json_dataYesOriginal JSON data
levelNoCompression level to test

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

C2.5/5.0
Behavior2/5

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

No annotations provided, and the description only says 'Calculate.' It does not disclose side effects, whether the tool is read-only, or any rate limits. For a calculation tool, read-only nature should be explicit.

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

Conciseness3/5

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

The description is extremely brief, consisting of four words. While concise, it lacks structure and fails to include essential details for effective use.

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?

No output schema is provided, and the description does not explain what metrics are returned. Given the complexity of compression metrics, the description is incomplete for an agent to understand the full behavior.

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 coverage is 100%, so parameters are well-documented. However, the description adds no additional meaning beyond the schema. It does not explain expected formats or constraints for json_data or level.

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

Purpose3/5

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

The description states 'Calculate detailed compression metrics and savings,' which identifies a verb and resource but is vague. Among sibling tools like estimate_savings, it does not clearly distinguish itself, making selection ambiguous.

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 on when to use this tool versus alternatives such as estimate_savings or analyze_patterns. No context on prerequisites, typical use cases, or outcomes.

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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