mcpField
P
Proseify

Proseify

Remote
proseify.xyzProductivity1Updated 5w ago

About Proseify

Proseify is a hosted MCP server for AI-assisted book writing, aimed at authors using MCP clients. It guides work from genre selection and literary references through outlining, chapter drafting, and scored revision.

Key features

  1. 01Genre recipes based on literary traditions
  2. 02Searchable public-domain book corpus
  3. 03Premise-based chapter outlines
  4. 04Book drafting in one workflow
  5. 05Genre-benchmarked evaluation and revision

Use cases

  1. 01Develop a novel from a one-line premise
  2. 02Find stylistic references for a genre
  3. 03Draft chapters with a planned structure
  4. 04Evaluate and revise a manuscript

MCP support

The hosted MCP server exposes tools for genre discovery, corpus search, style references, book planning, drafting, and evaluation to compatible clients.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.proseify.xyz/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

Details

From the project README

Proseify — the book-writing engine for AI agents

Proseify block mark

Proseify is a hosted MCP (Model Context Protocol) server that turns Claude Code, Codex, Cursor, or any MCP client into a disciplined book-writing machine — grounded in real literature instead of generic model defaults.

Give it a one-line premise. It picks the right genre recipe, pulls model passages from a curated public-domain library, plans the outline, drafts chapter by chapter, and scores its own work. You get a manuscript you can actually edit, not 40 pages of "it was a dark and stormy night."

🔗 Website & key issuance: https://proseify.xyz

Listed on mcpservers.org M8ven Score


Why genre-before-prose matters

Most "AI writes a book" tools generate immediately. The output reads the same way regardless of what you asked for — the same sentence lengths, the same dialogue rhythm, the same endings.

Proseify flips the order:

premise → genre recipe → corpus passages → outline → chapter drafts → scored revision

The genre recipe is derived from the actual tradition — pacing beats, dialogue ratio, sentence-length profile, how endings are handled. The agent reads it before touching prose. That single ordering decision is the difference between a horror book that reads like the gothic tradition and a horror book that reads like ChatGPT.

Tools exposed

ToolWhat it does
list_genres11 genres, growing library of books (theatre, horror, romance, adventure, literary, and more)
search_corpusFull-text search across all chapters, with FTS5 syntax (quotes, AND/OR/NOT, wildcards)
get_genre_recipePacing beats, dialogue ratio, and stylistic anchors derived from the tradition
get_style_referencesModel passages from books matching a genre — the register you're aiming for
plan_bookFull chapter-by-chapter outline from a one-line premise
write_bookThe one-shot flow: plan → draft → evaluate, all in one session
evaluate_bookScores a drafted book against genre benchmarks so weak chapters get revised

…plus supporting tools for chapter retrieval and corpus metadata.

Setup (Claude Code, Codex, Cursor, and friends)

1. Get a key

Sign in at https://proseify.xyz (Google / GitHub / magic link), pick a plan. Monthly from $9, or one-time Founding Lifetime. Your key is issued the moment the purchase clears.

2. Connect the MCP server
{
  "mcpServers": {
    "proseify": {
      "type": "http",
      "url": "https://mcp.proseify.xyz/mcp",
      "headers": { "Authorization": "Bearer YOUR_KEY" }
    }
  }
}

(Use the client's native MCP config format for your agent — see the Agent SDK page for a filled-in block.)

3. Install the skill
npx skills add https://proseify.xyz --skill anti-prose-slop -y

The anti-prose-slop skill gives the agent the writing workflow — genre recipe first, corpus grounding, chapter discipline, and a per-chapter anti-slop editing pass — instead of just raw tool access.

4. Write

Tell the agent a premise:

"I want a gothic horror novel about a lighthouse keeper finding a body that shouldn't exist. 32 chapters. Budget 50,000 words."

The agent picks the right recipe, searches the corpus for register, plans, drafts, scores, and revises. You edit what it returns.


The corpus

a growing library of books / 11 genres, drawn from Project Gutenberg and similar public-domain collections — all verified against their original headers, so there is no copyright ambiguity anywhere in the library. Commercial use of output built on it is safe.

A partial list: works from Austen, Stevenson, Hugo,conrad, Verne, the Brontës, Shelley, gothic masters, and more — selected, grouped by genre, and indexed for search.


Why "anti-prose-slop"?

Because the failure mode of AI-written prose isn't bad grammar — models are word-perfect. It's sameness: "seemed to", "began to", stacked adverbs, repeated sentence constructions, filter words, dialogue-as-exposition, chapter openings that always start with weather. The skill that ships with Proseify has a concrete per-chapter checklist against each of these, and evaluate_book scores the result against genre norms so the agent can self-revise rather than ship its first pass.


Pricing

PlanPriceRequests
Starter$9/mo120 req/min
Pro$19/mo400 req/min
Studio$49/mo9999 req/min
Founding Lifetimeone-time400 req/min, all genres

Cancel any time from your account (opens our payment provider's customer portal, signed in as you). See the refund policy for the 14-day money-back window.


License

MIT — see LICENSE. The public-domain corpus texts themselves are public domain (mostly Project Gutenberg-sourced).

Links

Comments

Markdown supported

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.