Moltline Humanizer MCP server

• ai_tell_scanFree

  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": "<draft>"} 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": "<what is wrong and how to fix it>"} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

• burstiness_reportFree

  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": "<draft>"} 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": "<what is wrong and how to fix it>"} (for example {"error": "need 3+ sentences"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

• humanize_planFree

  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": "<draft>", "fingerprint": <voice_fingerprint result>} 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": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

• verify_rewriteFree

  Verify a rewrite actually improved: score delta, meaning check, voice distance. PREMIUM (license). Compares reads-human score before/after, a meaning-preservation proxy, and (with a fingerprint) numeric distance to the target voice. Typical input {"original": "<draft>", "rewrite": "<edited draft>"} returns {"score_before": N, "score_after": N, "score_delta": N, "content_word_retention_pct": N, "remaining_tells": [...], "verdict": "Improved — ship it" | "Marginal — ..."}. Use only when both the before and the after text are available. Not for scoring a single draft (ai_tell_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "both texts must be non-empty"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

• voice_fingerprintFree

  Build a measurable voice profile from samples of a person's real writing. FREE. Feed it 2+ samples (emails, posts, essays — 150+ words total) and use the result with humanize_plan / verify_rewrite. Typical input {"samples": ["<email text>", "<blog post>"]} returns {"label": "my-voice", "target_metrics": {"avg_sentence_len": ..., "burstiness": ..., ...}, "favorite_words": [...], "signature_habits": ["..."], "words_analyzed": N}. Use on samples the person actually wrote, to build a target profile. Not for scoring an unknown draft (ai_tell_scan) and not on text the person did not write. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "need 150+ words of real writing across the samples"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 582ms20/20
• Tool poisoning5 tool descriptions checked15/15
• Auth qualityopen endpoint, read-only tools10/15
• Maintenancelast push 1 days ago15/15
• Maintainer identityendpoint served from the registry-verified namespace domain8/10

Safety score

91/100

higher than 85% of 48,255 servers

Tools

5

the typical server has 7

Tools that change things

0%

typical: 0%. Fewer write tools means less an agent can break

Uptime

100%

of our recent checks; the typical remote server answers 100%

Response time

205 ms

faster than 63% of 30,031 servers

Moltline Humanizer: common questions

Is Moltline Humanizer MCP server safe?

Yes, by our scan: it is graded A (91/100). Read the Moltline Humanizer safety report

How do I install Moltline Humanizer?

It runs remotely at mcp.moltlinestudio.com. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.

Does Moltline Humanizer need an API key?

Not as far as the registry entry and our scan can tell: no credentials are declared or required.

Is Moltline Humanizer maintained?

The last commit was 21 days ago (2026-10-14). The latest release is v1.2.0.

Is Moltline Humanizer up?

100% of our last 28 checks got an answer. We check remote servers about four times a day.

How does Moltline Humanizer compare with other MCP servers?

safety score 91/100 (higher than 85% of 48,255 servers); tools 5 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.