Scrivener MCP

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Scrivener MCP lets your AI assistant open, read, edit, analyze, and search your Scrivener projects directly. No copy-pasting. No exporting. Tell your assistant which project to open, and start working.

You: Open my novel and analyze the pacing in Chapter 12.

Claude: Opens your .scriv project, reads Chapter 12, runs pacing analysis. The first half moves well with short, tense paragraphs. The middle section slows considerably -- the three-page internal monologue starting at paragraph 14 stalls the momentum you built in the confrontation scene. Consider cutting it to a single paragraph and moving the backstory to Chapter 8 where Elena is first introduced.

Works with Claude Desktop, Claude Code, VS Code (Copilot/Continue), Cursor, and any MCP-compatible client. Scrivener 3 on macOS, Windows, and Linux. Listed on the official MCP Registry as io.github.writerslogic/scrivener-mcp.

Install

Pick the method that works for you. Most auto-configure Claude Desktop on install. Claude Code and other clients need one extra step -- see Claude Code below.

npm (recommended)

npm install -g scrivener-mcp

Restart Claude Desktop. Done.

Claude Code

Installing the npm package does not register the server with Claude Code -- the install-time auto-config only writes Claude Desktop's config. After installing, register the server:

npx scrivener-setup

This detects Claude Code (along with Claude Desktop and Cursor) and writes the config for you. To register it manually instead:

claude mcp add -s user scrivener -- npx scrivener-mcp

Then restart Claude Code (or run /mcp to reconnect) and Scrivener MCP appears in the server list. Drop -s user to scope it to the current project instead of all projects.

Smithery

npx -y @smithery/cli install scrivener-mcp --client claude

npx (no install)

Use directly without installing globally:

npx scrivener-mcp

Or add to your Claude Desktop config manually:

{
  "mcpServers": {
    "scrivener": {
      "command": "npx",
      "args": ["scrivener-mcp"]
    }
  }
}

GitHub

Install directly from the repo (latest main):

npm install -g writerslogic/scrivener-mcp

Or a specific release:

npm install -g writerslogic/scrivener-mcp#v0.12.0

Homebrew (macOS)

brew install writerslogic/tap/scrivener-mcp

Docker

docker build -t scrivener-mcp https://github.com/writerslogic/scrivener-mcp.git
docker run -i --rm -v /path/to/your/projects:/projects scrivener-mcp

Run the interactive setup to auto-detect and configure your client:

npx scrivener-setup

This detects Claude Desktop, Claude Code, and Cursor, and writes the config for you.

For other MCP clients, point them at npx scrivener-mcp as a stdio server.

Core features (document management, deterministic analysis, keyword search, and project memory) work without any API key. AI-powered analysis, generation, enhancement, and semantic search work with an Anthropic (Claude), OpenAI, or OpenRouter key; when several are present, Claude handles chat and generation (set AI_PROVIDER=openai or AI_PROVIDER=openrouter to override). OpenRouter defaults to the anthropic/claude-sonnet-4.6 model; set OPENROUTER_MODEL to use another model in its catalog. If the active provider fails with an account-level error (invalid key, exhausted credit, outage), the server automatically retries the request on the next configured provider. When your MCP client supports the sampling capability, supported chat-based AI features can also run through the client's own model—with no separately configured API key. Semantic indexing and similarity scoring use the local Holographic Memory System rather than an external embedding API, while the current semantic_search pipeline uses the configured chat provider to interpret queries and explain results. The server automatically discovers keys from common locations:

• ANTHROPIC_API_KEY / OPENAI_API_KEY / OPENROUTER_API_KEY environment variables
• ~/.env, ~/.scrivener-mcp/.env
• ~/.anthropic/key, ~/.openai/key, ~/.openrouter/key
• macOS Keychain (service names anthropic-api-key / openai-api-key / openrouter-api-key)

To store a key in the macOS Keychain:

security add-generic-password -s anthropic-api-key -a anthropic -w sk-ant-your-key-here

Or export it manually:

export ANTHROPIC_API_KEY="sk-ant-..."   # or OPENAI_API_KEY="sk-..."

This enables provider-backed writing analysis, content enhancement, generation, semantic search, character consistency checking, and intelligent compilation.

What You Can Do

First, open a project. The server acts on whatever .scriv project you point it at -- it has no link to the Scrivener app and can't see what you have open there. Start a conversation with "Open my Scrivener project at ~/Documents/My Novel.scriv" (or "Discover my Scrivener projects" if you don't know the path), then give your commands. On macOS you can also just say "Use the project I have open in Scrivener" -- it detects the open project and opens it (the first time, macOS asks you to allow controlling Scrivener). Do this once at the start of each conversation; the examples below all assume a project is open. If the same project is also open and unsaved in the Scrivener app, save or close it there first to avoid conflicting writes.

Manage Your Manuscript

Open any Scrivener project and work with it naturally. Read chapters, create new scenes, reorganize the binder, update synopses -- all through conversation.

You: Create a new scene called "The Reveal" after Chapter 5, and move the old epilogue to the trash.

Analyze Your Writing

Get detailed feedback on readability, pacing, style, dialogue quality, and emotional arc. Not generic advice -- analysis grounded in your actual prose.

You: Analyze Chapter 3. Is the pacing too slow?

Claude: Readability is good (Flesch-Kincaid grade 8.2), but pacing flags:

• 4 consecutive paragraphs of internal monologue (lines 45-78) with no action or dialogue
• The scene is 3,200 words with only 2 scene breaks -- your other chapters average 4
• Filter word density is 2x your manuscript average ("felt", "seemed", "noticed") Specific suggestions: ...

Enhance Your Prose

Apply targeted improvements: eliminate filter words, strengthen verbs, vary sentence structure, add sensory details, convert telling to showing, tighten dialogue, adjust pacing.

You: Eliminate the filter words in Chapter 7 and strengthen the verbs.

Track Characters and Plot

Store character profiles, plot threads, and style guides that persist with your project. The AI remembers your characters across sessions.

You: Save a character profile for Marcus: retired detective, cynical but fair, walks with a limp from an old injury, speaks in clipped sentences.

Later...

You: Check if Marcus is consistent across all chapters.

Claude: Found an inconsistency: Marcus walks "briskly" in Chapter 9 (line 34), but his limp is referenced in Chapters 2, 5, and 11. Also, his dialogue in Chapter 4 uses long flowing sentences, which contradicts the "clipped sentences" note in his profile.

Search by Meaning

Find passages by what they're about, not just keyword matching. "Find scenes where the protagonist feels isolated" works even if the word "isolated" never appears. The project index and similarity scoring run locally through the Holographic Memory System; the current search pipeline also uses your configured AI provider for query interpretation and result explanations, so semantic_search requires a provider.

You: Find all scenes where Elena and Marcus are alone together.

Track Relationships

Store and query relationships between characters, locations, themes, and plot threads. No Neo4j required -- relationships live in the semantic memory engine and persist with your project.

You: Who is connected to Marcus? What plot threads involve the lighthouse?

Compile and Export

Combine chapters into a single manuscript with configurable formatting, separators, and structure preservation. Export the result inline as Markdown, HTML, or JSON, or write a DOCX, EPUB, or PDF file to disk for submission, e-readers, or print.

All Tools

57 tools organized by workflow. To keep token usage low, tools load progressively -- project tools at startup, document and search tools when you open a project, and the rest on demand (your AI client activates them automatically, or calls them directly and the owning skill activates on the fly). Set SCRIVENER_MCP_EAGER_TOOLS=1 to load everything at once.

Tool	What it does
open_project	Open a .scriv project (accepts .scriv folders or .scrivx files) and make it active
discover_projects	Scan common locations for Scrivener projects when you don't know the path
detect_open_project	Detect the project currently open in the Scrivener app (macOS) so you don't need a path
get_structure	Browse the binder hierarchy (folders, documents, word counts)
refresh_project	Reload from disk after external edits
close_project	Close the active project and flush pending changes
verify_project_integrity	Read-only scan for structural problems (missing/duplicate UUIDs, unreadable content)
get_compile_settings	Read the project's compile formats and taxonomy -- labels/statuses (with colors), collections, section types
get_manuscript_briefing	One "where am I?" snapshot: words vs. target (% to goal), document/status/label counts, longest/shortest documents
list_snapshots	List Scrivener snapshots (title, date) for one document or the whole project
read_snapshot	Read a snapshot's text as plain text, with word count
compare_snapshot	Diff a snapshot against the current document (or another snapshot): paragraphs added/removed and net word change
create_snapshot	Take a Scrivener-native snapshot of a document (restorable from Scrivener's own Snapshots browser) before editing

Tool	What it does
get_document_info	Metadata for one document (title, type, word count, synopsis, label, status)
read_document	Read content; format: "formatted" for rich text, offset/limit to page long docs
write_document	Replace a document's content (atomic, with pre-write backup)
create_document	Create a new text document or folder
update_document	Change title and/or metadata (synopsis, notes, label, status, custom fields)
move_document	Reorganize within the binder
delete_document	Move to trash (reversible)

Tool	What it does
search	Keyword/full-text search; field: "title" for titles, scope: "trash" for trash
semantic_search	Find passages by meaning using the local HMS index plus provider-backed query interpretation, with similarity scores
find_mentions	Locate every occurrence of a specific name or term, with context
list_trash	List trashed documents
restore_document	Restore a document from trash
read_annotations	Read a document's comments and footnotes

Tool	What it does
analyze_document	AI writing analysis; focus with aspects (structure, style, pacing, themes...)
check_consistency	Project-wide continuity check; scope for plot, characters, or timeline
analyze_writing_style	Style-focused analysis
check_plot_consistency	Plot-thread consistency check
suggest_improvements	AI-generated improvement suggestions
enhance_content	Suggest a specific improvement to a document
generate_content	Generate new prose from a prompt and context
set_writing_goal	Set a word-count goal (daily, weekly, or whole project) with an optional target date
get_writing_goals	List goals with progress -- percent complete, words remaining, on-pace status
set_writing_preferences	Set author preferences (tone, complexity, length, POV, style guide) that steer AI output
get_writing_preferences	Show current preferences plus feedback insights and suggestions
collect_feedback	Record a rating/comment on an AI operation to inform those insights

Enhancement types: eliminate-filter-words, strengthen-verbs, vary-sentences, add-sensory-details, show-dont-tell, improve-flow, enhance-descriptions, strengthen-dialogue, fix-pacing, expand, condense, rewrite

Tool	What it does
compile_documents	Combine documents; mode: "structured" compiles the Draft folder with the binder hierarchy as headings and honors "Include in Compile" (no AI), mode: "intelligent" for AI-optimized output
export_project	Write the manuscript to disk -- Markdown, HTML, JSON inline, or DOCX, EPUB, PDF as a file
get_statistics	Project-level word/document/character counts
generate_marketing_materials	Draft synopsis, query letter, pitch, and related materials

Tool	What it does
remember	Store information that persists across sessions with the project
recall	Retrieve previously stored memory

Memory is stored within each .scriv project and travels with it.

Tool	What it does
add_relationship	Store a relationship between characters, locations, themes, or plot threads
find_relationships	Query entities related to a given character/theme/location
discover_connections	Find co-occurring entities across the manuscript
character_network	The character relationship network
get_entity_references	Trace the reference graph in either direction: entities a document mentions (by documentId), or documents mentioning an entity (by entity)
find_orphaned_entities	List registered characters/locations that no document actually mentions
suggest_connections	Suggest entities a document may be missing, inferred from cross-document co-occurrence

Works without Neo4j -- relationships live in the Holographic Memory System and are available immediately. The document cross-reference tools are fully deterministic (exact whole-word matching, no AI) and need no external services; Neo4j adds advanced graph analysis when connected.

Tool	What it does
queue_document_analysis	Enqueue an async analysis of one document; returns a job id
queue_project_analysis	Enqueue an async analysis of the whole project
get_job_status	Poll progress/results for a queued job
cancel_job	Cancel a queued or running job

Tool	What it does
list_skills	List the available tool groups and their tools
use_skill	Activate a tool group (most are pre-activated by default)

Guides

• Getting Started -- Installation, configuration, your first session
• MCP Client Setup -- Copy-paste config for Claude Desktop, Claude Code, Cursor, and VS Code
• Writing with AI -- Analysis workflows, enhancement strategies, memory management
• Troubleshooting -- Common issues and fixes
• Token Optimization -- How the server minimizes context window usage
• Architecture -- How the server works, module structure, data flow
• Scrivener Compatibility -- Supported Scrivener versions, platforms, and format coverage
• Scrivener File Format -- The reverse-engineered .scriv format, what we read vs. infer, and safe-modification guidance
• Fuzzing -- Jazzer.js target and OSS-Fuzz integration details
• Contributing -- Development setup, code conventions, adding new tools

Requirements

• Node.js 18+
• Scrivener 3 project files (.scriv)
• macOS, Windows, or Linux
• Optional: Anthropic, OpenAI, or OpenRouter API key for provider-backed AI features
• Optional: Neo4j for persistence and advanced graph queries; core relationship tools work without it

Development

git clone https://github.com/writerslogic/scrivener-mcp.git
cd scrivener-mcp
npm install
npm run dev          # Development mode with hot reload
npm run build        # Compile TypeScript
npm test             # Run tests
npm run typecheck    # Type checking only

Why This One?

Several Scrivener MCP servers exist. Feature claims below come from each project’s public documentation, published package, and advertised tool surface, last re-read on 2026-08-22; stars, forks, activity, and published version were refreshed 2026-08-22. “No” means the project does not document that capability; it does not claim the capability is impossible through the connected AI client.