Humanizer

MCP server providing psycholinguistic, lexical, structural, and discourse-level rules for producing naturally human-sounding written content. Part of the Human, an Education Collective MCP ecosystem.

Demo

Real output from humanizer_check_text on a stock piece of marketing copy. Two hard blocks, seven findings to clear, each with a fix. The server flags and suggests; it never rewrites the text.

Try it live in your browser → The checker is ported to TypeScript and runs entirely client-side, with no server and no network. It is verified to produce the same findings as the Python engine (see web/).

What It Does

The reference Claude consults when writing or revising content that needs to read as human-authored. It supplies rules, patterns, substitutions, and before/after examples. It does not rewrite text itself.

Data Dimensions

File	Contents
foundation.json	Absolute rules (AR-001/002/003), core principle, two axes, priority hierarchy, application protocol
lexical_patterns.json	Flagged words, substitutions, vocabulary rules
structural_patterns.json	Sentence length, syntax, punctuation, POS targets
sentiment_tone.json	Neutral bias, emotional layering, subjectivity
discourse_cohesion.json	Transitions, markers, cohesive devices, repetition
psycholinguistic_texture.json	Cognitive load, self-monitoring, retrieval
content_profiles.json	Per-content-type adjustments
anti_patterns.json	Before/after AI → human rewrites
caveats.json	ESL, detector brittleness, ethical considerations
self_review.json	Human-likeness self-review rubric the calling model runs on its own draft (the manual_review list)

Tools

This is a public read-only MCP. There are no runtime write tools. Data updates flow through a git push to this repo, then a container restart on the host. There is no tool-level auth.

Reference readers (12): humanizer_get_guide (full composite), humanizer_get_summary (lightweight), humanizer_get_application_protocol (ordered traversal plan), humanizer_get_foundation, humanizer_get_lexical_patterns, humanizer_get_structural_patterns, humanizer_get_sentiment_tone, humanizer_get_discourse_cohesion, humanizer_get_psycholinguistic_texture, humanizer_get_content_profile, humanizer_get_anti_patterns, humanizer_get_caveats

Checker (1): humanizer_check_text runs a deterministic compliance check. Hard violations (block prohibitions_clear): em-dashes, stacked punctuation, banned phrases. Clearable findings (must_clear): comma splices and other punctuation issues, flagged terms, sentence-level banned structures (the BS-* regex/heuristic tells), rule-of-three density, cross-section uniformity, reality-assertion (credibility) density, and low burstiness. Read-only with respect to server state: it analyzes the supplied text and returns a findings report; it does not rewrite or store anything.

Quick Start

pip install -r requirements.lock
PORT=8016 python src/server.py

Regenerating the lock file

requirements.txt is the human-edited input with loose top-level bounds. requirements.lock is the deploy artifact and is what the Dockerfile installs. The deployed image runs Python 3.12, so the lock must be resolved against Python 3.12. A lock generated under a different Python version can pin wheels that don't exist for cp312, which breaks the Docker build.

Recommended (uv handles the Python version explicitly):

uv pip compile --python-version 3.12 requirements.txt -o requirements.lock

Alternative (pip-tools, which must run inside a Python 3.12 environment):

python3.12 -m venv .venv-3.12
source .venv-3.12/bin/activate
pip install pip-tools
pip-compile --output-file=requirements.lock requirements.txt
deactivate

Deployment

See docs/DEPLOYMENT.md for the Docker Compose plus reverse-proxy or tunnel setup. The server is stateless and read-only, so it also runs fine as a single local container for development.

Endpoint

Once deployed, connect any MCP client to https://<your-host>/mcp with no auth.