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humanizer-mcp

humanizer-mcp

by aousabdo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

humanizer_analyze_ai_tells

Read-onlyIdempotent

Scans text for AI-generated patterns and computes a detection risk score with specific fix recommendations.

Instructions

Analyze text for AI-generated patterns and compute a detection risk score.

Scans for AI-associated vocabulary, structural patterns, burstiness, contraction usage, paragraph uniformity, rhetorical questions, first-person voice, and em dash frequency. Returns a comprehensive report with a 0–100 risk score and specific recommendations.

Args: params (AnalyzeTextInput): Contains the text to analyze.

Returns: str: JSON report with risk score, detected patterns, and fix recommendations.

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_analyze_ai_tells"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and no destruction. The description adds details about the analysis process, risk score range (0-100), and output contents (patterns, recommendations), which goes beyond annotations.

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 front-loaded with the main purpose, lists patterns, and explains output briefly. It is concise without unnecessary detail, though slightly verbose with 'Args:' and 'Returns:' sections.

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?

Despite the lack of output schema, the description explains that the return is a JSON report with risk score, detected patterns, and recommendations. This is sufficient for a simple input tool with one parameter.

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?

The input schema provides a description for the 'text' parameter. The description's 'Args' section only reiterates that 'params' contains the text, adding no new meaning beyond the schema. Baseline 3 is appropriate given schema coverage.

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 tool's purpose: 'Analyze text for AI-generated patterns and compute a detection risk score.' It lists specific patterns scanned and returns a score, which distinguishes it from siblings like humanizer_humanize_text or humanizer_compare_before_after.

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 implies usage for detecting AI-generated text, but does not explicitly state when to use this over sibling tools. However, the context of analysis vs. rewriting is clear enough for effective selection.

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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