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API Hub MCP — 337+ Tools

API Hub MCP — 337+ Tools

by scotia1973-bot
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

casualize

Convert formal or stiff text into a casual, conversational tone. Make your writing sound more natural and approachable.

Instructions

Convert formal or stiff text to casual, conversational tone.

Parameters:
    text — Text to convert to casual tone.

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": "casualize"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.4.1

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description should disclose behavioral traits beyond the basic function. It only states the conversion purpose, omitting details about how the transformation works, potential side effects, or limitations.

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 concise and front-loaded, consisting of two succinct sentences. It efficiently conveys the core purpose without unnecessary words.

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

Completeness3/5

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

For a simple text transformation tool, the description provides basic completeness. However, it does not mention the output format or return value, which is relevant since an output schema exists. More context about the transformation's nature (e.g., AI-based) would improve completeness.

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 adds meaning to the single parameter 'text' by explaining its role ('Text to convert to casual tone'), which goes beyond the schema's bare title. Given only one parameter, this is adequate.

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 converts formal/stiff text to casual tone, using a specific verb and resource. However, it does not explicitly distinguish from similar siblings like 'formalize' or 'simplify_text'.

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. It simply describes the action without context or exclusion criteria.

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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