Zero Slop MCP Server
io.github.manavmishra/zero-slop
Score and rewrite AI-assisted prose while preserving names, numbers, quotes, links, and facts.
What is the Zero Slop MCP server?
Zero Slop is an MCP server that detects and removes AI slop—overused phrases, vague language, and promotional wording—from AI-generated text while preserving core facts and details. It uses a 294-pattern scorer and eight-stage editing workflow to guide rewrites that reduce writing scores from 76+ down to 12-13 on average. The server is free, open-source, and available as a hosted MCP endpoint or local tool.
Zero Slop helps you identify and eliminate common AI writing patterns—canned openers, significance inflation, vague attribution, and promotional language—without losing your message's core intent. It's designed for anyone using AI to draft professional content, marketing copy, or other prose who wants to remove the telltale signs of machine-generated text. The tool combines local pattern detection with an optional AI-guided rewriting workflow and fact-checking gates to ensure edits preserve names, numbers, quotes, links, and structure.
How to install Zero Slop
Copy-paste configuration for popular MCP clients.
Tools & capabilities
Tools this server exposes to the agent.
deslop— Detects and rewrites AI slop from input text, returning a deslopped version with a writing score (0–100, lower is better) and source-detail verification.inspect— Flags sloppy passages and patterns in text without performing a rewrite, showing which phrases triggered detection.score— Scores text offline using 294 weighted patterns and a 96-term lexicon, returning a 0–100 writing score without sending data to a model.
Use cases
- Remove AI slop from LinkedIn posts, email drafts, and professional announcements before publishing
- Audit batches of AI-generated content to identify which pieces need human review before use
- Teach Zero Slop your writing preferences by providing original, edited, and reason for changes to build a private learning profile
- Score files locally offline to check against a review threshold (e.g., gate 25) without model calls
- Integrate Zero Slop into CI/CD pipelines or content workflows via REST API or CLI to enforce writing quality standards
Zero Slop MCP server FAQ
Zero Slop detects and removes AI slop—overused phrases, vague language, and promotional wording—from AI-generated text. It scores prose on a 0–100 scale (lower is better), flags problematic patterns, and guides rewrites while preserving facts, names, numbers, quotes, and links.
Yes, Zero Slop is free and open-source under the MIT license. It offers a browser editor, CLI tool, local scoring, and a hosted MCP endpoint at https://mcp.zero-slop.ai/mcp with shared free capacity.
In Claude Code or Codex, run `npx skills add manavmishra/ZeroSlop --global` and use `/zero-slop (your writing)`. In Claude.ai, upload the skill ZIP from the latest release. For MCP-compatible clients, connect to https://mcp.zero-slop.ai/mcp.
No authentication is required for the free hosted MCP endpoint or local tools. The shared free capacity accepts up to 20,000 Unicode code points per request.
It detects 294 weighted patterns including canned openers ('We're thrilled to…'), vague attribution ('experts agree'), significance inflation ('marks a pivotal moment'), promotional wording ('robust,' 'seamless'), binary contrast formulas, repeated sentence shapes, crowded statistics, and overworked formatting.
No. Zero Slop is a slop detector and editor, not an authorship detector. It flags patterns worth reviewing but cannot determine whether text was written by a human or AI.
README (reference)
Source of truth, from the repository.
Zero Slop
Find and remove AI slop in your writing. Get rid of workslop without losing your core intent and message.
Zero Slop is a free, open-source agent skill that finds and removes AI slop while checking that the core details of your message survive the edit. If your AI setup does not support agent skills, try the browser editor or use our MCP connector.
<p align="center"> <a href="https://github.com/manavmishra/ZeroSlop/actions/workflows/validate.yml"><img alt="Validate" src="https://github.com/manavmishra/ZeroSlop/actions/workflows/validate.yml/badge.svg"></a> <img alt="Version 2.12.12" src="https://img.shields.io/badge/version-2.12.12-72528F?color=C15732"> <a href="https://www.npmjs.com/package/zero-slop"><img alt="npm version" src="https://img.shields.io/npm/v/zero-slop?color=C15732"></a> <a href="https://www.npmjs.com/package/zero-slop"><img alt="npm downloads" src="https://img.shields.io/npm/dm/zero-slop?color=17634F"></a> <a href="https://github.com/manavmishra/ZeroSlop/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/manavmishra/ZeroSlop?style=flat&color=C15732"></a> <a href="LICENSE"><img alt="MIT license" src="https://img.shields.io/badge/license-MIT-141412"></a> <a href="https://hol.org/registry/plugins/manav-mishra%2Fzero-slop"><img alt="Listed in the HOL plugin registry" src="https://img.shields.io/badge/HOL%20registry-listed-2C6E8F"></a> <a href="https://hol.org/guard/plugins?badge=manav-mishra%2Fzero-slop"><img alt="Verify Zero Slop on HOL Guard" src="https://img.shields.io/badge/HOL%20Guard-verify%20listing-2C6E8F"></a> </p>Why it exists
An AI draft can be grammatically sound and still read like workslop. In a compatible AI assistant, Zero Slop flags slop patterns, then guides the edit. The writing score finds patterns worth reviewing; it cannot tell who wrote the text.
<a href="assets/zero-slop-demo.mp4?v=dark-shell-restored-20260906"> <picture> <source media="(prefers-reduced-motion: reduce)" srcset="assets/zero-slop-demo-poster.png?v=dark-shell-restored-20260906"> <source type="image/webp" srcset="assets/zero-slop-demo.webp?v=dark-shell-restored-20260906"> <img src="assets/zero-slop-demo.gif?v=dark-shell-restored-20260906" width="900" alt="Dark-shell demo: install Zero Slop, edit with your assistant, and check scores while preserving 40%."> </picture> </a>See an edit
Let's see Zero Slop at work. Imagine using AI to write a linkedin launch announcement and getting this:
We're thrilled to announce that our team has leveraged cutting-edge machine learning to deliver a seamless onboarding experience, reducing setup time by 40%.
The local Python scorer in Zero Slop scores the input Slop score 99.3/100. A high Slop score means the draft is more likely to contain sloppy patterns.
Zero Slop then strips the patterns and guides the AI agent to produce the deslopped output below:
We used machine learning to reduce onboarding setup time by 40%.
Writing score: 9.5/100 [clear]
Flagged phrases : 0 across 10 words
Quick start
You can try the browser editor without installing anything, install the skill in an assistant that supports skills, or use the hosted service through MCP and the API.
If you use Claude Code, Codex, or another assistant that supports skills, here's how to install Zero Slop there:
npx skills add manavmishra/ZeroSlop --global
/zero-slop (your writing)
To see the flagged passages without an edit, use /zero-slop inspect (your writing).
The command above works with Claude Code and Codex. In Claude.ai, upload the skill ZIP. Other installation paths include Gemini CLI and remote MCP connections where your client allows them. You can also score a file locally without a model call.
What it does
Your AI assistant, whether Claude, GPT, or another compatible model, reads and edits the draft. The skill supplies the workflow and local tools: a 0 to 100 writing score, source-detail checks, and a final comparison with the original.
What it catches
The scorer uses 294 weighted patterns and a 96-term lexicon. It checks for:
- binary contrast formulas: “It's not X. It's Y.”
- canned openers: “We're thrilled to…” and “Here's the thing…”
- vague attribution: “experts agree” and “studies show”
- significance inflation: “marks a pivotal moment” and “a testament to”
- promotional wording: “robust,” “seamless,” and “leverage” when used as hype
- repeated sentence shapes, crowded statistics, and overworked formatting
The editing workflow
The Zero Slop agent uses an eight-stage workflow. Each stage is a job with a role, not a separate model; some run in the Python tools and others run in the user's AI app. We treat eight stages as an engineering convention, not eight separate models.
| Stage | Job |
|---|---|
| 1. Scorer | Find exact phrases, pacing problems, readability issues, and overworked formatting. |
| 2. Interpreter | Read the claims, audience, structure, and voice before editing. |
| 3. Rewriter | Remove stock language without inventing detail. |
| 4. Fact gate | Check names, numbers, quotations, links, code, tables, paths, and structure locally. |
| 5. Copy desk | Fix grammar, usage, spelling, and consistency. |
| 6. Read-aloud editor | Catch stumbles, repetition, and awkward transitions. |
| 7. Verifier | Compare the edit with the source for meaning, qualifiers, voice, and format. |
| 8. Fresh-eyes finalizer | Apply only safe final polish, then run one last local check. |
Evidence and limits
A saved, same-model editing test
We ran Zero Slop and three other open-source agent skills on our AI Slop test corpus, using GPT-5.4, high reasoning, and pinned instructions. Saved outputs are reproducible.
| Method | Mean writing score ↓ | Passed local gates | Source check passed | Mean length change |
|---|---|---|---|---|
| Original drafts | 76.3 | 0/18 | — | — |
| Zero Slop | 12.8 | 18/18 | 18/18 | -8.9% |
| avoid-ai-writing | 23.3 | 15/18 | 18/18 | -14.6% |
| no-ai-slop | 28.4 | 12/18 | 17/18 | -13.7% |
| humanizer | 35.4 | 9/18 | 17/18 | -7.2% |

The RAID+ audit asks a different question: how much default writing from different models is flagged as AI slop by Zero Slop? The test corpus contains 7,627 anonymous, user-generated transcripts:
| Model | Texts scored | Mean writing score ↓ | At or above 25 |
|---|---|---|---|
| DeepSeek V3 | 1,995 | 14.5 | 10.1% |
| Gemini 3.1 Pro | 1,998 | 17.0 | 18.2% |
| Gemma 3 27B | 1,634 | 21.6 | 30.4% |
| Llama 3.3 70B | 2,000 | 25.5 | 41.7% |
RAID+ records which model wrote each passage, not whether it reads well.
Documented features
This is a feature comparison of Zero Slop against other popular slop tools.

The checks draw on research into predictable machine wording and overused vocabulary. Zero Slop cannot identify an author: detectors can misclassify non-native English.
Private learning
You can teach Zero Slop a preference by giving it the original output, your edited version, and the reason for the change. Private data stays under $ZERO_SLOP_HOME and follows your privacy settings. Zero Slop is an AI slop detector and editor, not a plagiarism tool.
For developers: other ways to access Zero Slop
Hosted MCP
The endpoint for compatible clients is:
https://mcp.zero-slop.ai/mcp
For Gemini CLI, run gemini extensions install https://github.com/manavmishra/ZeroSlop --auto-update. For file-upload assistants, download the single-file bundle.
Local scoring
Score a file without sending it to a model:
npx zero-slop score draft.md
From a cloned checkout, check a folder against the review threshold of 25:
python3 scripts/slopscore.py --batch drafts/ --gate 25
Command line
The CLI sends a file to the hosted editor without changing the file on disk:
npx --yes zero-slop@2.12.12 deslop draft.md --genre professional
Use - for stdin and --json for structured output. --require-approved prints the result but exits nonzero when review is needed. Requires Node.js 22+; offline score also needs Python 3. CLI options and privacy.
REST API
The REST API accepts the same edit request:
curl --fail-with-body --max-time 75 https://mcp.zero-slop.ai/v1/deslop \
-H 'Content-Type: application/json' \
--data '{"text":"Maya owns the pricing review.","genre":"professional"}'
Check status before using an edit. Shared free capacity accepts up to 20,000 Unicode code points after trimming. API reference · OpenAPI contract
Find the source
| Path | Purpose |
|---|---|
SKILL.md | The complete detect, rewrite, verify, and learn workflow |
scripts/slopscore.py | Offline meter and source-detail gate |
scripts/register.py | Performed-register and reading pass |
references/ | Genre guidance, tells, safeguards, and evaluation rules |
examples/ | Reproducible before-and-after edits |
bench/ | Frozen benchmarks, provenance, and limitations |
mcp/ | Optional hosted MCP server documentation |
DISTRIBUTION.md | Direct installs, marketplace submissions, and release synchronization |
Contribute or get help
Found a false positive, a broken check, or a better example? Use the issue forms or start a Discussion. If you want to change a pattern, read CONTRIBUTING.md and include tests with your pull request.
For setup help, see SUPPORT.md. Report security issues through SECURITY.md.
Credits
Zero Slop builds on ideas from First Reader, no-ai-slop, humanizer, de-slop, stop-slop, unslop-text, and avoid-ai-writing.
License
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