Moltline Humanizer — Voice-Matching Editor

Humanize Plan

humanize_plan

Read-onlyIdempotent

Produce a precise rewrite brief that de-AIs a draft, with numeric targets. PREMIUM (license).

Lists every flagged tell with its fix and sentence-rhythm surgery targets; when a voice_fingerprint result is supplied, adds numeric targets to hit that person's voice. Apply the brief with your agent, then confirm with verify_rewrite. Typical input {"text": "", "fingerprint": } returns {"current_score": 0-100, "edit_list": ["..."], "numeric_targets": {...}, "process": ..., "integrity_note": ...}.

Use after a scan has identified what to fix; returns a brief, not rewritten prose. Not for checking whether a rewrite worked (verify_rewrite). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. 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 AI draft to plan a rewrite for.
`fingerprint`	No	Optional voice profile object exactly as returned by voice_fingerprint; omit for a style-only pass.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A5/5.0

Behavior5/5

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

The description discloses error handling behavior: 'never raises a protocol error — it returns {"error": ...}' and confirms read-only, idempotent nature. This adds value beyond annotations (readOnlyHint, idempotentHint) by explaining retry safety and error format. No contradiction with 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 well-structured: starts with a crisp purpose sentence, then details output, usage, error handling, and safety. Every sentence adds necessary information without redundancy. It is appropriately sized for the tool's complexity.

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 nested parameters and an output schema, the description covers input/output format, error handling, usage context, sibling relationships, and license restrictions. It is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant context: typical input example, relationship between text and fingerprint parameters, and expected output structure. This helps the agent understand how to supply the fingerprint parameter and what to expect in return.

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: 'Produce a precise rewrite brief that de-AIs a draft, with numeric targets.' It distinguishes from siblings by explicitly noting its role after a scan, and contrasting with verify_rewrite for checking rewrites.

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 when-to-use guidance: 'Use after a scan has identified what to fix' and what not to use for: 'Not for checking whether a rewrite worked (verify_rewrite).' It also explains the tool returns a brief, not rewritten prose, and mentions retry safety after error correction.

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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