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API Hub MCP — 337+ Tools

API Hub MCP — 337+ Tools

by scotia1973-bot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

calculate_entropy

Compute the Shannon entropy of a string to quantify its randomness. Useful for cryptography, data analysis, and compression tasks.

Instructions

Calculate Shannon entropy of a string (measure of randomness/information content).

Parameters:
    text — String to calculate entropy of.

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": {
      "text": "<text>"
    },
    "name": "calculate_entropy"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.4.1

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It mentions the calculation is Shannon entropy but does not disclose any additional behavioral traits like side effects, input length limits, or output format. The existence of an output schema partially compensates.

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 extremely concise, consisting of a single sentence about purpose followed by a parameter listing. Every word serves a purpose, and the format is easy to parse.

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

Completeness4/5

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

For a simple single-parameter tool with an output schema (likely returning a number), the description covers the concept and parameter adequately. It could mention entropy range or behavior for empty strings, but overall it is sufficient for an AI agent.

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

Parameters4/5

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

The description lists the only parameter 'text' and states it is the string to calculate entropy of. Since schema description coverage is 0%, this minimal added context is helpful. However, it does not specify constraints like maximum length or character encoding.

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

Purpose5/5

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

The description clearly states the specific function: calculating Shannon entropy of a string, which measures randomness or information content. This verb+resource combination is distinct from sibling tools like char_count or word_frequency.

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 explains what the tool does but provides no guidance on when to use it versus other text analysis tools (e.g., analyze_readability, text_stats). It lacks any 'when to use' or 'alternatives' statements.

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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