noveletary

日本語

novel + secretary — a constraint-maintained narrative knowledge base and MCP server that checks the internal consistency of fiction, with first-class support for Japanese prose.

What

A local MCP server an LLM (Claude Code, Claude.ai Projects) calls while you write or import a novel. It tracks story facts in an append-only operation log, gates contradictions at write time, branches parallel plot drafts, and routes the questions it cannot decide to you — the author.

• Construction = checked mutation. Adding a fact runs hard-constraint checks (a dead character acting, a monotone ledger decreasing, a temporal cycle, an orphaning delete) and rejects contradictions with the conflicting fact set.
• Verification = the same engine, batch mode. Audit a whole branch; hard violations are certain, optional semantic checks (NLI) become author questions.
• Story branches are first-class: parallel drafts (A-plot / B-plot) audited independently, merged with structural conflict detection, rolled back without losing history.
• The author is the oracle. Unresolved aliases, merge conflicts, and semantic doubts go to the author, not to LLM guesswork. Answers persist and propagate through later checks.

Why

LLM-driven consistency checking is wasteful and unreliable when the LLM both writes and self-grades. noveletary keeps a deterministic constraint engine and the author as the trusted core, and treats the LLM as a fallible translator with no authority. Most "contradictions" in fiction are structurally decidable (state machines, numeric invariants, temporal constraints) and need no semantics; the semantic residual is the only part a language model judges, and even then the verdict is a question, not a gate.

Status

Early (v0.1). Core engine, store, branching, merge, audit, and MCP server are implemented and tested. The Japanese NLP extraction layer (GiNZA reconciliation; KWJA zero-anaphora adapter) is optional and advisory; the KWJA path awaits its upstream model host. Not yet deployed remotely (Cloudflare Workers + D1 is a known migration path).

Install

pip install -e ".[dev]"          # core + tests
pip install -e ".[dev,nlp]"      # add Japanese NLP (GiNZA, KWJA)

Run as an MCP server

noveletary-mcp                                   # stdio
claude mcp add noveletary -- noveletary-mcp      # register with Claude Code

SQLite state persists in data/narrative.db (run from the repo root; override with NARRATIVE_DB).

Tools (LLM-facing)

Tool	Purpose
get_state / get_log	state before writing (chapter-sliced, subject-focused), history
add_fact / add_facts	register facts (hard-gated) — writing from scratch
import_facts	bulk-load an existing work (not gated) — then audit surfaces issues
update_fact / delete_fact	supersession (+retcon check) / delete (orphan check)
audit	hard violations always; include_soft=True adds NLI-based author questions
create_branch / merge_branches / rollback_branch	parallel drafts, structural merge, non-destructive rollback
list_open_questions / answer_question	the author-oracle channel
extract_facts / reconcile_facts	independent prose extraction; cross-check the LLM's self-report

License

MIT. See LICENSE.