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Moltline Humanizer MCP Server

answering

Moltline Humanizer is answering right now. Last checked 11 min ago. It exposes 5 tools. Last commit 18 Aug 2026.

Find AI-isms with evidence and fingerprint a writing voice from samples. 3 of 5 free.

Uptime history 15 days of history · worst day 98%
15 days agonow
98.9%
Uptime 24h
90 of 91 checks
5
Tools
read from the server
276 ms
Response time
average over 24h
0
Stars
last commit 18 Aug 2026

Moltline Humanizer does not always answer

Over the last week it answered 99.8% of our checks. We check every 15 minutes, so you hear about the next outage within the hour — not from your users.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 11 min ago.

run in your terminal
claude mcp add humanizer --transport http https://mcp.moltlinestudio.com/humanizer
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "humanizer": {
      "url": "https://mcp.moltlinestudio.com/humanizer"
    }
  }
}
~/.codex/config.toml
[mcp_servers.humanizer]
url = "https://mcp.moltlinestudio.com/humanizer"
.cursor/mcp.json
{
  "mcpServers": {
    "humanizer": {
      "url": "https://mcp.moltlinestudio.com/humanizer"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "humanizer": {
      "url": "https://mcp.moltlinestudio.com/humanizer"
    }
  }
}

Available tools 5

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

burstiness
burstiness_report
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
humanize_plan
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.
tell
ai_tell_scan
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.
verify
verify_rewrite
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
voice_fingerprint
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.

Endpoints

URLTransportStateLatencyChecked
https://mcp.moltlinestudio.com/humanizer streamable-http answering 189 ms 11 min ago

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Moltline Humanizer — questions

Answers built from our own checks of this server.

What can Moltline Humanizer do?
It exposes 5 tools, read directly from the server on our last check. Among them: ai_tell_scan, burstiness_report, humanize_plan, verify_rewrite, voice_fingerprint. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is Moltline Humanizer working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 90 of 91 checks got a reply (98.9%), average response time 276 ms. The bar chart above shows every period we have measured.
How do I connect Moltline Humanizer?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.
Does Moltline Humanizer need an API key?
No. Moltline Humanizer completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 5 of them are readable on this page. This is what we observed, not what the docs claim.
How fast is Moltline Humanizer?
It answers our handshake in 276 ms on average, which is faster than 61% of all working MCP servers we measure. The comparison comes from our own checks across the whole registry, every 15 minutes.
Is Moltline Humanizer open source?
Yes — it is published under the MIT licence, written in Python and 0 stars on GitHub. The source link is on this page, so you can read exactly what it does with your data before you connect it.