An MCP service for AI-assisted reasoning and editing on long-form fiction projects.

Designed to work with OpenClaw but compatible with any MCP-capable AI gateway.

For local stdio MCP clients, run the published package directly:

WRITING_SYNC_DIR=/path/to/sync-dir DB_PATH=./writing.db npx -y @hanna84/mcp-writing

The CLI wrapper defaults to stdio transport and adds the Node 22 SQLite flag automatically when needed.

For VS Code-native setup flows (including prose styleguide setup), use:

• hannasdev/mcp-writing-vscode

Instead of feeding an entire manuscript to an AI and hoping it fits in the context window, mcp-writing builds a structured index from your scene files. The AI queries that index first — finding relevant characters, beats, and loglines — then loads only the specific prose it needs.

Current status:

• Core platform complete: Metadata-first analysis, sidecar-backed metadata maintenance, AI-assisted prose editing with confirmation + git history, review bundles, and Scrivener Direct extraction are all implemented.
• Recently delivered: Guideline generation is now delivered and tracked in done PRDs.
• Active development: OpenClaw integration is the current focus area.
• Deferred backlog: embeddings search is intentionally deferred for later exploration.

• Novelists and writing teams working on long manuscripts with many scenes, characters, and continuity constraints.
• AI-assisted editing workflows where you want targeted context retrieval instead of full-manuscript prompting.
• Projects that need traceable, reversible edits with metadata that stays synchronized as drafts evolve.

describe_workflows now exposes an outcome-first, discovery-first workflow map. This is a breaking change if your prompts or automation depend on previous workflow IDs or ordering.

Update integrations using this mapping:

• manuscript_exploration -> question_driven_discovery (or targeted_scene_reading when the task is prose inspection)
• prose_editing -> safe_scene_revision
• character_management -> character_understanding
• place_management -> place_understanding
• review_bundle -> review_preparation

New workflow IDs added:

• thread_understanding
• parity_recovery

Styleguide workflows are still available, but no longer positioned as part of the primary daily workflow surface.

find_scenes and get_arc now always return structured envelopes, including non-paginated calls.

• Envelope fields: results, total_count.
• Pagination fields are included when paging is active.
• warning / next_step are included when relevant.

If your integration previously handled raw arrays for non-paginated calls, update it to parse envelopes consistently.

Safe parsing pattern:

const parsed = JSON.parse(toolText);
if (parsed.ok === false) throw new Error(parsed.error?.message ?? "tool error");
const scenes = parsed.results ?? [];
const totalCount = parsed.total_count ?? scenes.length;
const warning = parsed.warning ?? null;
const nextStep = parsed.next_step ?? null;

These metadata-read tools now return structured envelopes instead of flat objects or raw arrays.

• get_character_sheet and get_place_sheet: previously returned a flat object of field values; now return { results: [row], total_count: 1, next_step }.
• list_scene_references: previously returned { references, scene_id, project_id }; now returns { results, total_count, scene_id, project_id }.
• get_relationship_arc: previously returned a raw JSON array; now returns { results, total_count, from_character, to_character }.

Safe parsing pattern for sheet tools:

const parsed = JSON.parse(toolText);
if (parsed.ok === false) throw new Error(parsed.error?.message ?? "tool error");
const sheet = parsed.results?.[0] ?? {};
const nextStep = parsed.next_step ?? null;

Safe parsing pattern for list/arc tools:

const parsed = JSON.parse(toolText);
if (parsed.ok === false) throw new Error(parsed.error?.message ?? "tool error");
const items = parsed.results ?? [];
const totalCount = parsed.total_count ?? items.length;

Goal: catch inconsistencies before sharing pages.

1. Run sync after your latest writing session.
2. Ask find_scenes for scenes involving a specific character or tag (for example, all scenes tagged injury or promise).
3. Use get_arc to review that character's ordered progression across the manuscript.
4. Load only the suspect scenes with get_scene_prose.
5. Attach follow-up notes with flag_scene where continuity needs a fix.

Outcome: you review one narrative thread at a time instead of rereading the entire novel to find contradictions.

Goal: make sure subplot threads progress intentionally and resolve on time.

1. Run list_threads for the project.
2. Use get_thread_arc to inspect scene order and beat labels for each thread.
3. When a beat is missing, call upsert_thread_link to add or update it on the right scene.
4. Re-run get_thread_arc to confirm pacing and coverage.

Outcome: subplot structure stays visible and auditable, which reduces dropped threads in late drafts.

Goal: keep indexes accurate without manually re-tagging everything.

1. After rewriting scenes, call enrich_scene to re-derive lightweight metadata from current prose.
2. Use update_scene_metadata for intentional editorial fields (for example, beat, POV, timeline position, and tags).
3. Use search_metadata and find_scenes to verify scenes are discoverable under the expected filters.

Outcome: your AI assistant can reliably find the right scenes without drifting from the manuscript.

Goal: let AI propose prose edits without losing control of your draft.

1. Ask the AI to call propose_edit for a specific scene.
2. Review the staged diff.
3. Accept with commit_edit or reject with discard_edit.
4. Use list_snapshots (and optional snapshot_scene) to inspect or preserve revision history.

Outcome: you get AI speed with explicit approval and recoverable history for every applied change.

Goal: rebuild scene-to-character links in a controlled way after imported prose changes or metadata drift.

1. Start with enrich_scene_characters_batch using the default dry_run=true to preview inferred links for a project, chapter, or explicit scene list.
2. Poll get_async_job_status until the batch job completes, then review job.result.results for changed scenes, ambiguous matches, and partial failures.
3. Spot-check a few affected scenes with get_scene_prose if the changes touch important continuity or cast-heavy chapters.
4. Re-run enrich_scene_characters_batch with dry_run=false once the preview looks correct.
5. If you want a destructive overwrite instead of additive merge behavior, use replace_mode=replace with confirm_replace=true deliberately.

Outcome: character-link maintenance becomes a preview-first batch operation instead of a one-off regex script or manual sidecar cleanup.

Goal: recover index confidence quickly when legacy upgrade warnings indicate ambiguous rows were skipped.

1. Start by checking get_runtime_config (or describe_workflows) and confirm whether db_migration_warnings contains LEGACY_JOIN_ROWS_SKIPPED.
2. If present, run sync immediately to rebuild scene relationships from current sidecars and prose metadata.
3. Continue normal discovery (find_scenes, get_arc, get_thread_arc) and watch for stale-metadata warnings.
4. When you touch stale scenes, run enrich_scene(scene_id, project_id) to recover metadata parity incrementally.
5. If many scenes remain stale, switch to enrich_scene_characters_batch (dry-run first) for broader catch-up.

Outcome: upgrade-related data loss risk becomes an explicit, operator-visible recovery workflow instead of a silent state mismatch.

AGPL-3.0-only