About publishready
PublishReady analyzes AI-generated drafts for writers and teams preparing publication. Its local, deterministic metrics, target checks, and evidence-based revision suggestions avoid external API calls and model-dependent scores.
Key features
- 01Deterministic style and readability metrics
- 02Template, target, and reference comparisons
- 03Ranked revision suggestions
- 04Audit of AI-sounding prose markers
- 05Fiction and non-fiction analysis
Use cases
- 01Checking drafts against publication targets
- 02Finding sentence and wording revisions
- 03Reviewing prose for formulaic markers
MCP support
The MCP server exposes 16 specialized writing-analysis and control tools, including deterministic analysis of AI-sounding prose.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
16Saved tool list · updated 2w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| text | string | Yes | English prose or markdown-ish content to analyze. Blank input is accepted and returns zeroed metrics. |
| options | object | No | Optional flags for expanding the structured response. |
| options.include_sentence_details | boolean | No | Include per-sentence structural details in the `details.sentences` array. |
| options.include_formula_breakdown | boolean | No | Include formula inputs, contributors, and linked revision levers in the response. |
| options.include_paragraph_details | boolean | No | Include per-paragraph structural details in the `details.paragraphs` array. |
Details
From the project READMEPublishReady: Professional Writing Control
PublishReady is a deterministic writing analysis system designed to turn AI drafts into publish-ready writing. It serves as the final QA pass for AI-generated prose, providing local-first metrics, target compliance, and specific revision levers without sending text to remote services.
The PublishReady Packages
This project is structured as a professional, layered monorepo containing specialized packages:
Core Packages
- @veldica/publishready-mcp: The Model Context Protocol (MCP) server implementation (
publishready-mcp). - @veldica/publishready-cli: The command-line tool for local analysis (
publishready). - @veldica/publishready-core: The central orchestration engine.
- @veldica/publishready-schemas: Unified Zod schemas and explicit interfaces.
Underlying Libraries
- @veldica/prose-analyzer: Deterministic style signals (variety, density, repetition, narrative texture).
- @veldica/readability: Consolidated library of all major readability formulas.
- @veldica/prose-tokenizer: Standalone markdown-aware prose tokenization.
- @veldica/prose-linter: Target checks, revision levers, content integrity, and deterministic AI-sounding prose markers.
Installation
MCP Server (Recommended)
Add the server to your MCP client configuration:
{
"mcpServers": {
"publishready": {
"command": "npx",
"args": ["-y", "@veldica/publishready-mcp"]
}
}
}
Command Line
npx @veldica/publishready-cli analyze sample.txt
Hosted MCP
For Smithery, VPS, or gateway deployments, run the server with Streamable HTTP:
npx @veldica/publishready-mcp --transport=http --port=3000
The MCP endpoint is /mcp; the health endpoint is /health.
Key Features
- Template, Target, and Reference Modes: Compare writing against built-in templates, explicit numeric targets, reference text, or reusable reference profiles.
- Deterministic Metrics: Structural counts, sentence and paragraph distributions, lexical signals, scannability, fiction proxies, and readability formulas.
- Specific Revision Levers: Ranked, evidence-based suggestions such as
shorten_long_sentences,replace_difficult_words, andreduce_abstract_wording. - AI-Sounding Prose Audit: Deterministic marker inventory for formulaic, generic, or over-polished prose, including exact matches and tracked phrase counts.
- Fiction & Non-Fiction Support: Narrative metrics for dialogue, sensory density, abstract wording, and scene pacing.
- Explainable Interpretation: Target and metric interpretation that explains audience, use cases, style implications, and tradeoffs.
- Local-First & Private: Stdio-first, deterministic, no external API calls, and no LLM wrappers.
MCP Tool Surface
The MCP server exposes 16 specialized tools for analysis and control, including audit_ai_sounding_prose for deterministic AI-marker analysis. For a full list and documentation, see the MCP README.
Deterministic Philosophy
This package explicitly avoids perplexity and other model-dependent scores. We believe writing control should be:
- Explainable: You should know exactly why a score changed.
- Reproducible: The same text should always yield the same metrics.
- Practical: A metric is only useful if it tells you what to change.
Development
npm install
npm run build
npm run lint
npm run typecheck
npm test
Publishing Metadata
- npm package:
@veldica/publishready-mcp - MCP Registry name:
io.github.veldica/publishready - Product homepage: https://veldica.com/publish-ready
- Source repository:
https://github.com/veldica/publishready-mcp
Registry and Directory Metadata
PublishReady is prepared for MCP directory discovery through:
- GitHub repository topics
- npm package keywords
- Glama metadata via
glama.json - Official MCP Registry metadata via
mcpName
This MCP is designed for AI-assisted writing workflows where the model should improve clarity, structure, readability, and publish-readiness while preserving facts, terminology, intent, and author voice.
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.