Moltline Humanizer — Voice-Matching Editor

Ai Tell Scan

ai_tell_scan

Read-onlyIdempotent

Scan a draft for the measurable tells of AI-generated prose. FREE.

Flags stock phrases (with exact quotes), structural reflexes, uniform sentence rhythm, em-dash overuse, and hedging boilerplate — every flag cites the actual text. Typical input {"text": ""} returns {"reads_human_score": 0-100, "metrics": {"burstiness": ..., "avg_sentence_len": ..., ...}, "evidence": [{"type": "stock_phrase", "quote": "..."}], "note": "..."}.

Use for a first read on whether a draft carries machine-writing signals. Reports measurable patterns, not a verdict on who wrote the text, and must not be used to accuse a person of anything. Not for rhythm detail (burstiness_report) or for a rewrite brief (humanize_plan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The draft to scan — at least one full sentence of plain text.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint as true. The description reinforces these ('Every call is read-only and idempotent') and adds critical behavioral detail: no protocol errors on invalid input, returns structured error object with fix instructions, and outlines the return format (reads_human_score, metrics, evidence, note). No contradictions.

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?

Every sentence serves a purpose: purpose, flagged patterns, example, usage guidance, siblings, error handling. It is front-loaded and structured. Slightly verbose in listing flags, but each item adds clarity. Overall efficient for the amount of information conveyed.

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 tool's single parameter and the presence of an output schema, the description leaves no gaps. It covers purpose, what it detects, input/output shape, valid input, error behavior, usage guidelines, and sibling distinctions. The output schema exists but the description still summarizes the return structure for quick reference.

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?

There is only one parameter (text) with 100% schema description coverage. The tool description adds value beyond the schema by including a typical input example and clarifying the minimum requirement ('at least one full sentence'). This extra context helps the agent understand valid usage beyond the schema's basic type constraint.

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: 'Scan a draft for the measurable tells of AI-generated prose.' It lists specific patterns it flags (stock phrases, structural reflexes, rhythm, em-dash overuse, hedging) and explicitly distinguishes itself from sibling tools burstiness_report and humanize_plan.

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

Usage Guidelines5/5

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

Provides explicit usage context: 'Use for a first read on whether a draft carries machine-writing signals.' Includes when-not-to-use: 'must not be used to accuse a person,' and names alternatives for related tasks. Also gives error-handling guidance: on invalid input it returns an error object and is always safe to retry.

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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TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: scanning for tells, mapping rhythm, producing a rewrite plan, verifying rewrites, and building a voice profile. Descriptions explicitly indicate usage boundaries (e.g., 'not for rhythm detail', 'not for scoring a single draft').

Naming Consistency3/5

Names use snake_case but vary in structure: verb+noun (verify_rewrite, humanize_plan), noun+noun (burstiness_report, voice_fingerprint), and a compound noun (ai_tell_scan). While still readable, the lack of a consistent verb_noun pattern lowers coherence.

Tool Count5/5

Five tools is well-scoped for a specialized humanization editor. Each tool adds a necessary step in the workflow—from analysis to planning to verification—without redundancy or missing functionality.

Completeness5/5

The tool set covers the full lifecycle: analysis (ai_tell_scan, burstiness_report), targeting (voice_fingerprint), planning (humanize_plan), and verification (verify_rewrite). No obvious gaps; the rewrite execution is delegated to the agent, which is a sensible design choice.

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