Moltline Humanizer — Voice-Matching Editor

Burstiness Report

burstiness_report

Read-onlyIdempotent

Map a draft's sentence rhythm and find where it goes flat. FREE.

Reports per-sentence lengths, the burstiness coefficient, and runs of similar-length sentences. Typical input {"text": ""} returns {"sentence_lengths": [12, 14, 13, 5, 28], "burstiness": 0.52, "flat_runs": [{"sentences": "1-3", "lengths": [12, 14, 13]}], "tip": "..."}.

Use when prose reads flat and sentence-length pattern is the suspect. Not for a full inventory of tells (ai_tell_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need 3+ sentences"}). 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 analyze; needs at least 3 sentences.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as true/false accordingly. The description goes beyond these by detailing the error handling behavior (never raises protocol error, returns error object), the specific input format, and the retry advice, providing comprehensive behavioral context.

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 and well-structured: it starts with a clear purpose, includes a usage note, provides a typical input/output example, explains when to use (and when not), and describes error handling. Every sentence adds value with no 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 tool has only one parameter, full schema coverage, and all annotations present, the description is complete. It covers purpose, usage, input/output example, error handling, and read-only/idempotent nature.

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?

Schema coverage is 100%, so the schema documents the single parameter well. The description adds value by specifying the typical input format and the requirement for at least 3 sentences, which is already in the schema description but is reinforced with example structure.

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: to map a draft's sentence rhythm and identify flat spots. It explicitly distinguishes from the sibling tool 'ai_tell_scan' by noting this tool is not for a full inventory of tells.

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?

The description provides explicit guidance on when to use the tool ('when prose reads flat and sentence-length pattern is the suspect') and when not to use it ('not for a full inventory of tells'), including the name of the alternative tool. It also gives retry instructions after errors.

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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