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humanizer-mcp

humanizer-mcp

by aousabdo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

humanizer_get_rewrite_instructions

Read-onlyIdempotent

Analyze AI-generated text and receive step-by-step rewrite instructions to humanize it. Tailored to text type and voice preferences.

Instructions

Analyze AI text and return detailed, step-by-step rewrite instructions.

This tool does NOT rewrite the text itself — it provides a structured action plan that an LLM or human editor can follow to humanize the text. The instructions are tailored to the text type and specific patterns found.

Args: params (HumanizeTextInput): Contains text, text type, and voice preferences.

Returns: str: JSON with analysis results and step-by-step rewrite instructions.

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": {
      "params": "<params>"
    },
    "name": "humanizer_get_rewrite_instructions"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true) already indicate non-destructive behavior. The description adds that it returns instructions, not rewritten text, and that output is a JSON string with analysis and instructions, aligning with and complementing the annotations.

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 concise, front-loaded with purpose, and structured with Args/Returns sections. Every sentence adds value without redundancy.

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

Completeness5/5

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

Given the presence of an output schema (though not shown), the description's explanation of return value as 'str: JSON with analysis results and step-by-step rewrite instructions' is sufficient. The tool is well-contextualized among siblings and parameters are defined in schema.

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 0% as the description does not detail parameters, but the schema itself is rich with descriptions for each property (text, text_type, preserve_meaning, voice_notes). The description's mention of 'text, text type, and voice preferences' is a high-level summary, adding little beyond the schema.

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 'Analyze AI text and return detailed, step-by-step rewrite instructions' and explicitly distinguishes itself by noting 'This tool does NOT rewrite the text itself — it provides a structured action plan.' This differentiates it from sibling tools like humanizer_humanize_text.

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

Usage Guidelines4/5

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

The description implicitly guides usage by stating it provides instructions rather than rewriting, and mentions tailoring to text type and patterns. However, it does not explicitly list when to use this tool vs alternatives, though the sibling context helps.

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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