BookStack MCP Server

An MCP (Model Context Protocol) server that gives Claude full access to a BookStack instance. Built in Rust with tokio/axum as a Cargo workspace with pluggable database backends and optional semantic vector search.

Features

• Full CRUD on all core BookStack resources (shelves, books, chapters, pages, attachments)
• Full-text search with BookStack query operators
• Semantic vector search — natural language search across all content via embeddings (optional). Two modes on semantic_search: standard (default, vector + keyword + Markov-blanket blend) and precision (4-stage cascade — semantic → keyword → Markov-blanket → cross-encoder). An optional rerank: bool flag layers a cross-encoder pass on top of the standard top-N (v0.13.0; replaces the pre-v0.13.0 mode: "rerank"). Per-page access control enforced via BookStack's API on every result.
• Settings UI (/settings) — browser-based admin configuration page (token-gated via the same /authorize flow). Surfaces only the global server fields the index worker needs (hive_shelf_id, user_journals_shelf_id).
• Pluggable database — SQLite for simple deployments, PostgreSQL + pgvector for production
• Separate embedder — background embedding service with pluggable backends (local ONNX, Ollama, OpenAI, Voyage)
• Cross-encoder reranker (optional) — embedder exposes POST /rerank when BSMCP_RERANK_PROVIDER is configured. Three providers: local (in-process ONNX cross-encoder via fastembed, default BAAI/bge-reranker-v2-m3), voyage (Voyage's /v1/rerank), openai (any OpenAI-shape rerank endpoint — covers Voyage/Jina/Cohere-via-shim/self-hosted). Off by default; consumed by semantic_search's rerank: true flag (refinement on the standard mode) + mode: "precision" (cascade), and by search_content's rerank: true flag (v0.13.0).
• Server-side markdown to HTML conversion — send markdown, server converts before sending to BookStack
• Staging upload flow — upload local images and attachments through a two-step staging endpoint without exposing local paths to the container (see below)
• OAuth 2.1 support — use as a Claude.ai or Claude Desktop custom connector without config files
• Encrypted token storage — OAuth tokens encrypted at rest with AES-256-GCM
• Dual transport — SSE (MCP 2024-11-05) and Streamable HTTP (MCP 2025-03-26)
• Dynamic structure discovery — AI automatically learns your BookStack hierarchy on connect
• Auto-migration — seamlessly migrate from SQLite to PostgreSQL on startup
• Multi-user support via per-session BookStack API tokens
• Multi-arch Docker images (amd64 + arm64)

Architecture

crates/
  bsmcp-common/       Shared types, traits, config, chunking, vector utils
  bsmcp-db-sqlite/    SQLite backend (rusqlite, bundled)
  bsmcp-db-postgres/  PostgreSQL + pgvector backend (sqlx)
  bsmcp-server/       MCP server binary (axum, no ONNX dependency)
  bsmcp-embedder/     Embedder + reconciliation worker (single binary, role-selected via --role flag)
                      — local ONNX / Ollama / OpenAI / Voyage embedding, job queue worker, HTTP /embed + optional /rerank
                      — reconciliation worker: initial walk + webhook/cron delta walk on the index_jobs queue

docker/
  Dockerfile.server       Lightweight server image (~35MB)
  Dockerfile.embedder     Embedder + worker image with ONNX Runtime (~45MB)
  docker-compose.yml      PostgreSQL deployment (production)
  docker-compose.sqlite.yml  SQLite deployment (simple)

The MCP server handles all client-facing protocol, OAuth, and search. The embedder runs separately, polling a database-backed job queue to embed pages and serving a /embed HTTP endpoint for query-time embedding (and /rerank when a reranker provider is configured). The embedder supports four embedding backends: local ONNX models (fastembed), Ollama, OpenAI-compatible APIs, and Voyage. The reconciliation worker (same binary, run with --role=worker) owns the index_jobs queue — runs the initial full walk on cold start, then consumes webhook + cron jobs and the periodic delta walk. Run as two compose services (separate embedder + worker) or as one with --role=both.

Available Tools (59 BookStack + 3 semantic = 62)

Semantic tools (semantic_search, reembed, embedding_status) only appear when BSMCP_SEMANTIC_SEARCH=true and an embedder is running. Without semantic search: 59 BookStack tools.

The server is a thin BookStack CRUD facade plus semantic-search enrichment, OAuth, audit, and the reconciliation worker. Personal-memory primitives (journals, identities, reminders) and the v0.8.0/v0.9.0 briefing surface were removed in v0.10.0; v0.11.0 added the optional cross-encoder reranker on the embedder side; v0.13.0 (current) refactors that reranker from a third semantic_search mode into a flag on both semantic_search and search_content (breaking — see the v0.12 → v0.13 migration below). See the migration notes below.

Setup

Prerequisites

• A BookStack instance with API access enabled
• A BookStack API token (created in your BookStack user profile under "API Tokens")
• Docker and Docker Compose (for container deployment)

Quick Start (PostgreSQL — recommended)

cp .env.example .env
# Edit .env with your BookStack URL, encryption key, and database password

docker compose -f docker/docker-compose.yml up -d

This starts four containers:

• bsmcp-postgres — PostgreSQL 17 with pgvector extension
• bsmcp-server — MCP server (port 8080)
• bsmcp-embedder — Background embedding service (--role=embedder, default); also serves /rerank when a reranker is configured
• bsmcp-worker — Reconciliation worker (same image as the embedder, started with --role=worker): initial walk on cold start, webhook + cron job consumption, periodic delta walk

Quick Start (SQLite — simple)

cp .env.example .env
# Edit .env with your BookStack URL and encryption key

docker compose -f docker/docker-compose.sqlite.yml up -d

This starts three containers (server + embedder + worker) sharing a SQLite database file.

Run from source

The project distributes as multi-arch (linux/amd64 + linux/arm64) container images on GHCR — ghcr.io/bees-roadhouse/bsmcp-server and ghcr.io/bees-roadhouse/bsmcp-embedder. Native binaries for bsmcp-server only are attached to each GitHub Release for linux-x86_64, linux-aarch64, darwin-x86_64, darwin-aarch64, and windows-x86_64. The embedder is not distributed as a bare binary — it depends on ONNX Runtime (a per-platform C++ shared library), so running it outside Docker is awkward. Either run the published embedder container, or build from source:

# Server
cargo run --release -p bsmcp-server

# Embedder (separate terminal)
cargo run --release -p bsmcp-embedder

The server is pure Rust + bundled SQLite and builds cleanly on any target the Rust toolchain supports. The embedder depends on fastembed, which links ONNX Runtime; the crate downloads a matching prebuilt at build time for common targets, but cross-compiling or running on uncommon platforms may require installing ONNX Runtime separately. For most users, running the embedder from the published container avoids that complexity entirely.

Configuration

Server Variables

Embedder Variables

Role Selector

The embedder image hosts both the embedder loop and the reconciliation worker loop. Select which runs per container via --role (CLI flag, primary) or BSMCP_ROLE (env fallback). Default is embedder.

The CLI flag wins over the env. Setting both (compose command: + BSMCP_ROLE) is belt-and-suspenders — recommended for clarity in compose files.

Worker Variables

Read when the embedder is running with --role=worker or --role=both. The worker shares the same database as the server and owns the index_jobs queue.

* Required, but the fallback to BSMCP_EMBED_TOKEN_* covers most setups.

Job Lifecycle Variables

Read by the worker role's lifecycle housekeeper. Apply to both embed_jobs and index_jobs.

See .env.example for the full list with comments.

Semantic Search Setup

1. Set BSMCP_SEMANTIC_SEARCH=true in your server env
2. Set BSMCP_WEBHOOK_SECRET to a random string (16+ characters)
3. Create a BookStack API token with read access for the embedder (BSMCP_EMBED_TOKEN_ID / BSMCP_EMBED_TOKEN_SECRET)
4. Start the embedder container — it downloads the ONNX model (~1.3GB) on first run
5. Use the reembed tool (via Claude) to trigger initial embedding of all pages
6. Configure a webhook in BookStack for automatic re-embedding on page changes:

BookStack Webhook Configuration

Go to Settings > Webhooks > Create Webhook in your BookStack instance:

Events to select:

• Page Create
• Page Update
• Page Delete

Custom header (required for verification):

X-Webhook-Secret: YOUR_WEBHOOK_SECRET

The YOUR_WEBHOOK_SECRET value must match BSMCP_WEBHOOK_SECRET in your server environment. The server uses constant-time comparison to verify the header.

After saving, any page create/update/delete in BookStack automatically queues a re-embedding job. The embedder picks it up within seconds (configurable via BSMCP_EMBED_POLL_INTERVAL).

Reranker Setup (optional — for the rerank: true flag and mode: "precision")

The cross-encoder reranker is off by default. semantic_search (default mode: "standard", rerank: false) and search_content (default rerank: false) both work fine without it. To enable the rerank: true flag on either tool, or to use mode: "precision" on semantic_search:

1. Pick a provider and set BSMCP_RERANK_PROVIDER on the embedder to one of:
   • local — in-process ONNX cross-encoder via fastembed. No API key. Default model: BAAI/bge-reranker-v2-m3 (~600 MB, downloads on first run). Reuses BSMCP_MODEL_PATH for the cache directory.
   • voyage — Voyage AI's /v1/rerank. Set BSMCP_RERANK_API_KEY. Default model: rerank-2.
   • openai — any OpenAI-shape /v1/rerank endpoint (Voyage, Jina, Cohere-via-shim, self-hosted). Requires all of BSMCP_RERANK_API_KEY, BSMCP_RERANK_API_URL, and BSMCP_RERANK_MODEL (no upstream default — OpenAI itself has not shipped a rerank API).
2. Restart the embedder. On startup it logs Reranker: <provider> <model> and starts answering POST /rerank. Without configuration it logs Reranker: disabled (BSMCP_RERANK_PROVIDER unset or 'none') and /rerank returns 503.
3. Use the reranker from a tool call:
   • semantic_search with mode: "standard", rerank: true — runs the standard pipeline, then the cross-encoder re-orders the top-N. Equivalent to the pre-v0.13.0 mode: "rerank".
   • semantic_search with mode: "precision" — wider candidate pool, cross-encoder is the ranker of record (4-stage cascade; rerank is always on).
   • search_content with rerank: true — BookStack keyword search, then the cross-encoder re-orders the matched results. In all three cases the response includes scoring.rerank per result and stats.{rerank_ms, rerank_provider, rerank_model, candidates_reranked}. If the reranker is disabled, the server surfaces a clear error pointing at BSMCP_RERANK_PROVIDER so callers can drop the flag and retry.

Per-provider config blocks are documented under Reranker Providers.

Connecting

The MCP endpoint URL is:

https://your-host/mcp/sse

Important: Use the full path including /mcp/sse — not just the base domain.

Claude.ai (Custom Connector)

1. Go to Settings > Integrations > Add custom MCP in Claude.ai
2. Enter the MCP endpoint URL: https://your-host/mcp/sse
3. A login form opens in your browser — enter your BookStack API Token ID and Token Secret
4. Once authorized, BookStack tools appear automatically in your conversations

Claude Desktop (Custom Connector)

1. Add a custom connector with URL: https://your-host/mcp/sse
2. When connecting, a login form opens in your browser with instructions
3. Enter your BookStack API Token ID and Token Secret

No config files needed — authentication happens entirely through the browser via OAuth 2.1.

Claude Code (Direct Bearer Token)

Add to your MCP server configuration:

{
  "mcpServers": {
    "bookstack": {
      "url": "https://your-host/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN_ID:YOUR_TOKEN_SECRET"
      }
    }
  }
}

The token ID and secret come from your BookStack API token (created under My Account > Access & Security > API Tokens).

API Endpoints

Upgrading

All schema migrations are automatic on startup (CREATE TABLE IF NOT EXISTS, ALTER TABLE for new columns). No manual SQL is needed.

Heads up. v0.10.0 stripped the briefing layer + per-user settings; v0.11.0 added the optional cross-encoder reranker; v0.12.x was CI/build only; v0.13.0 (current) is breaking on two fronts — folds bsmcp-worker into bsmcp-embedder (single image, role-selected) AND refactors the reranker surface from a third semantic_search mode into a rerank: bool flag on both semantic_search and search_content. Older entries describe functionality that no longer ships and are kept only for upgrade-path archaeology.

From v0.12.x to v0.13.0 (current) — breaking on two fronts

What changed — worker fold (issue #56)

• bsmcp-worker and bsmcp-embedder are now one binary, one image. The embedder image (ghcr.io/bees-roadhouse/bsmcp-embedder:0.13.0) runs in either role depending on --role=embedder|worker|both (CLI flag, primary) or BSMCP_ROLE=embedder|worker|both (env var, fallback). Default with no flag is embedder — preserves v0.12.x behavior for unmigrated compose files that hit the embedder service.
• Indexer walks every visible shelf on every full walk. Per-call scoping (shelf_ids / book_ids / chapter_ids / page_ids / scopes on semantic_search) is the only scope surface — the v0.12.x hive_shelf_id / user_journals_shelf_id config + the v0.13.0-RC indexed_shelves interim are both gone. Issue #122 supersedes #119/#120: the briefly-shipped GlobalSettings::indexed_shelves field (empty = walk all, non-empty = walk subset) conflated indexing-time corpus selection with query-time result scoping. With #80's per-call scope params already shipped, callers narrow at the search call instead. The startup migration drops indexed_shelves_json from both backends (DROP COLUMN IF EXISTS — Postgres native, SQLite 3.35+); the v0.12.x hive_shelf_id + user_journals_shelf_id columns get the same idempotent drop. The /settings UI loses its indexed-shelves input. Operators who carved a narrow corpus via indexed_shelves will see the indexer expand to walk every visible shelf on next boot; keep search results scoped by passing shelf_ids (or a named kb_scopes entry) on each semantic_search call.
• bsmcp-worker Docker image is now an alias of bsmcp-embedder. Pulls of ghcr.io/bees-roadhouse/bsmcp-worker:0.13.0 resolve to the same manifest. This alias is one-release-only and will be removed in v0.14.0. Update your compose now.
• CI matrix drops the bsmcp-worker entry. Release builds are now two images: bsmcp-server + bsmcp-embedder.
• /health carries a new role field ("embedder", "worker", or "both") so operators can curl two compose services and verify the role flag actually took effect.

What changed — rerank as flag (issue #115)

• rerank: bool flag on semantic_search. Replaces the v0.11.0–v0.12.x mode: "rerank" value. Set rerank: true on mode: "standard" (or mode: "default") to get the same cross-encoder-on-top-of-standard behavior the old mode: "rerank" produced — same candidate pool, same /rerank call path, same scoring.rerank + stats.rerank_* response shape. Default false.
• rerank: bool flag on search_content. New: when true, the BookStack keyword results are reordered by the cross-encoder, each result picks up a scoring.rerank field, and stats.{rerank_ms, rerank_provider, rerank_model, candidates_reranked} is added to the response. Same shape as semantic_search's rerank surface — a caller can parse both tools' output with one parser. Default false.
• mode: "precision" keeps the cascade behavior unchanged. The cross-encoder is always on in precision mode by definition; passing rerank: true is a no-op there.

What's removed (breaking)

• semantic_search mode: "rerank" is hard-cut — there is no deprecation period. Callers passing mode: "rerank" get a structured error: mode: "rerank" was removed in v0.13.0. Pass rerank: truewithmode: "standard" instead — same cross-encoder pass, now a flag.

What's automatic

• No DB schema changes. No env-var renames. Existing BSMCP_INDEX_TOKEN_* / BSMCP_EMBED_TOKEN_* precedence is preserved (index tokens primary, embed tokens fallback).
• Existing semantics (FOR UPDATE SKIP LOCKED, retry chains, supersedence, lifecycle housekeeper) unchanged.
• Callers that never used mode: "rerank" see no behavior change from the search-surface refactor.

What you must do — compose

In your compose file, find the bsmcp-worker service and change two lines:

   bsmcp-worker:
-    image: ghcr.io/bees-roadhouse/bsmcp-worker:0.12.x
+    image: ghcr.io/bees-roadhouse/bsmcp-embedder:0.13.0
+    command: ["bsmcp-embedder", "--role=worker"]
     environment:
+      BSMCP_ROLE: worker
       BSMCP_DB_BACKEND: postgres

If you don't set --role=worker (or BSMCP_ROLE=worker), the container will boot in default embedder mode and contend with your real embedder for jobs. Setting both is belt-and-suspenders.

The bsmcp-server and bsmcp-embedder services only need the version bump to :0.13.0.

If you want to collapse to a single container, set --role=both on the embedder service and delete the worker service entirely.

What you must do — mode: "rerank" callers

• Replace {"mode": "rerank"} with {"mode": "standard", "rerank": true} (or {"rerank": true} — mode defaults to "standard"/"default"). Same cross-encoder pass, same response shape, just expressed as a flag. The 503 fallback on a missing BSMCP_RERANK_PROVIDER is unchanged.

Strict env hygiene

When the embedder boots with --role=worker, it emits a WARN worker_role_ignoring_embedder_env env=BSMCP_EMBED_PROVIDER line for each embedder-only env it finds set. Surfaces stale config you might have copied from the old worker service block. Not fatal — these envs are silently ignored under role=worker.

Footprint note

The bsmcp-worker role now ships ONNX Runtime (~150 MB on-disk; runtime memory unaffected because the worker role does not load the ONNX model). One-time disk cost per host; the image cache makes subsequent pulls free.

From v0.11.0 to v0.12.x

What's automatic

• No schema, env-var, or API surface changes. Pull new images, restart.

What you must do

• Nothing required. v0.12.1 is the recommended pull because it's the first release with all five native bsmcp-server binaries attached (aarch64-unknown-linux-gnu was missing from v0.11.0 and v0.12.0 due to a cross-build issue, now fixed).

From v0.10.0 to v0.11.0

What's new

• Cross-encoder reranker on the embedder. New POST /rerank endpoint when BSMCP_RERANK_PROVIDER is set on the embedder. Three providers: local (in-process ONNX cross-encoder via fastembed; default BAAI/bge-reranker-v2-m3), voyage (Voyage's /v1/rerank), openai (any OpenAI-shape rerank endpoint). Off by default — BSMCP_RERANK_PROVIDER=none (or unset) leaves the endpoint disabled and returns 503.
• Three-mode semantic_search (v0.11.0–v0.12.x — see v0.13.0 notes above for the breaking refactor into a flag). Replaces the prior single-shape behavior with mode: "standard" | "rerank" | "precision", defaulting to "standard":
  • standard — vector + keyword + Markov-blanket blend (the v0.10.0 default behavior).
  • rerank — same candidate pool as standard, but the final top-N is re-ordered by the cross-encoder. Cheap refinement (~10–30 ms for top-10 against a local cross-encoder). Hard-cut in v0.13.0 in favor of the rerank: bool flag on mode: "standard".
  • precision — wider initial vector pool (5× limit), no keyword/blanket blend, cross-encoder is the ranker of record. More expensive, can rescue hits the blend would miss.
• Per-result scoring.rerank and stats.rerank_* in the search response when either rerank-enabled mode fires (mode, hybrid, rerank_ms, rerank_provider, rerank_model, candidates_reranked).

What's automatic

• No schema changes. Drop-in at the database layer.
• Existing semantic_search callers keep working unchanged — mode defaults to "standard" and reproduces v0.10.0 behavior.

What you must do (only to use the new modes)

1. Pull new images: ghcr.io/bees-roadhouse/bsmcp-server:0.11.0 + ghcr.io/bees-roadhouse/bsmcp-embedder:0.11.0 + ghcr.io/bees-roadhouse/bsmcp-worker:0.11.0.
2. Set BSMCP_RERANK_PROVIDER (and the matching BSMCP_RERANK_MODEL / BSMCP_RERANK_API_KEY / BSMCP_RERANK_API_URL) on the embedder.
3. Pass mode: "rerank" or mode: "precision" on semantic_search calls. If the reranker is disabled, the embedder returns 503 and the server surfaces a clear error pointing at BSMCP_RERANK_PROVIDER so callers can drop back to mode: "standard". (As of v0.13.0, the mode: "rerank" value is hard-cut in favor of a rerank: bool flag — see the v0.12.x → v0.13.0 notes above.)

From v0.9.0 to v0.10.0

What's removed

• briefing MCP tool, POST /briefing/v1/read HTTP route, and the auto-injected meta.briefing envelope are all gone. The single-call reconstitution shell from v0.8.0 / v0.9.0 turned out to fan out 5+ parallel BookStack page fetches per request and fail open on stale system_prompt_page_ids config. Removed wholesale.
• Per-user UserSettings struct and the user_settings table (both Postgres and SQLite) — every consumer was the briefing path or related setup nudges. No per-user state to persist after the cut.
• Per-user role-level ACL filtering on semantic search and tools/list — depended on UserSettings.bookstack_user_id. Semantic search becomes user-anonymous on the embedder side; per-page access control still runs through BookStack's API on every result.
• user_role_cache table — fed only the per-user role-level filter.
• Briefing-only GlobalSettings fields (org_required_instructions_page_ids, org_ai_usage_policy_page_ids, org_identity_page_id, org_domains, guide_page_id, policies_scope, sops_scope, best_practices_scope, friendly_structure, full_content_in_briefing, strict_setup) and the matching /settings UI sections.
• Instance summary subsystem (Ollama caller + the Summary: … log lines + BSMCP_LLM_* / BSMCP_SUMMARY_* env vars).
• session_event and dismiss_setup_nudge MCP tools (briefing-only).
• try_auto_populate_bookstack_user_id in the OAuth flow — no settings row to populate.
• v0.7.x extras migration shims.

What survives

• 59 BookStack CRUD tools (create_* / update_* / delete_* / get_* / list_* + search_content).
• semantic_search, reembed, embedding_status.
• bsmcp-embedder + bsmcp-worker images and the reconciler.
• Rate limiter + audit log (#54 infra).
• OAuth 2.1 / /authorize flow.
• /settings admin UI for the surviving global server config (hive_shelf_id, user_journals_shelf_id).

Tool count

• With semantic search: 62 tools (59 CRUD + 3 semantic). Without semantic: 59.

What's automatic

• user_settings, user_role_cache, remember_audit, token_bindings, sessions tables are dropped on first startup (idempotent).
• Briefing-only global_settings columns are dropped via ALTER TABLE DROP COLUMN (Postgres native; SQLite ≥ 3.35).
• Existing org-level page-id config is discarded — re-enter via the trimmed /settings page if any of the surviving fields apply.

Breaking changes

• Clients calling briefing or /briefing/v1/read get tool not found / 404.
• Tool responses no longer carry meta.briefing.
• tools/list no longer filters by role.

From v0.8.0 to v0.9.0

What's new

• v1.0.0 rollback. The Phase 2 re-introduction of personal-memory MCP tools (user, config, directory, identity, journal, migrate, reminders, events, sessions, session_event, dismiss_setup_nudge) is gone. The single briefing tool from v0.8.0 stays. The codebase is back to v0.8.0's posture plus the issue #54 rate-limiter / job-lifecycle infrastructure.
• DB tables token_bindings and sessions are no longer created on fresh installs. Existing v1.0.0 deployments upgrading to v0.9.0 keep the on-disk tables (inert; DROP TABLE manually if cleanup matters).
• UserSettings shed the per-account-settings + journal-resolver fields added in v1.0.0 (account_label, use_org_identity, journaling_enabled, chosen_ai_identity, setup_complete, tool_overrides, user_journal_book_id, cached_user_email*, cached_first_name*, cached_is_admin*). The extras JSON catch-all silently preserves any leftover keys until the briefing's migration handler clears them.
• GlobalSettings.tool_defaults and admin_setup_complete dropped — admin-only defaults followed the per-tool toggle infrastructure into the bin.
• /setup/user and /setup/admin browser wizards removed. The /settings page is the only browser-side configuration surface.
• oauth.rs::ensure_token_binding reverted to v0.8.0's try_auto_populate_bookstack_user_id shape. Tokens key the user_settings row directly via token_id_hash again; no binding indirection.

What survives from the v0.8.0 → v1.0.0 era

• Rate limiter + job lifecycle (bsmcp_common::rate_limit, embed_jobs / index_jobs lifecycle columns, /jobs/{embed,index}/{id}/cancel endpoints, the lifecycle housekeeper in bsmcp-worker). Issue #54 work is general infra and is kept verbatim.

What's automatic

• No DB migration ships for v1.0.0 → v0.9.0 downgraders. CREATE TABLE IF NOT EXISTS won't re-shape the v1.0.0 user_settings PK (stable_id → token_id_hash). If a clean reset is needed, drop the table manually before first start, or wait for a follow-up one-shot migration.
• v0.8.0 → v0.9.0 is a no-op schema-wise.

From v0.7.4 to v0.8.0

What's new

• Personal-memory layer moved to memberberry.ai. All 12 remember_* MCP tools (remember_briefing / remember_journal / remember_collage / remember_shared_collage / remember_user_journal / remember_whoami / remember_user / remember_identity / remember_directory / remember_config / remember_audit / remember_search) no longer ship. The POST /remember/v1/{resource}/{action} HTTP namespace is gone.
• Single briefing MCP tool replaces the 12 remember tools. Same response shape as the old remember_briefing action=read, no action arg. HTTP form: POST /briefing/v1/read.
• meta.briefing auto-injection on every MCP tool response — full content on the first call per (token_hash, session_id), sticky bits (time + setup summary) thereafter. Calling briefing explicitly resets the session for the next response — useful after the AI's harness compacts the conversation.
• Typed setup slots on global settings — guide_page_id, org_identity_page_id, policies_scope, sops_scope, best_practices_scope, plus org-wide booleans friendly_structure, full_content_in_briefing, strict_setup. Idempotent ALTER TABLE ADD COLUMN migrations on first startup.
• Removed: default_ai_identity_* global columns (dropped via ALTER TABLE DROP COLUMN), remember_audit table (DROP TABLE IF EXISTS), and most per-user pointer fields from UserSettings (ai_*_book_id, user_journal_book_id, recent_*_count, etc.). The settings UI shrank ~1,300 lines to match.

What's automatic

• Idempotent ALTER TABLE ADD COLUMN for the new global slots.
• ALTER TABLE DROP COLUMN [IF EXISTS] for default_ai_identity_* (Postgres native; SQLite swallows duplicate-drop errors via .ok(), requires SQLite ≥ 3.35).
• DROP TABLE IF EXISTS remember_audit on startup.
• user_settings is a JSON blob — old keys are silently ignored on read and dropped on next save.

From v0.7.3 to v0.7.4

What's new

• Briefing payload trimmed — *_semantic_matches entries now cap at 3 chunks of ~100 chars each (kb_semantic_matches: 4 × 150). Truncated chunks carry truncated: true and a … suffix. A new top-level semantic_matches_hint field tells consumers to call get_page(page_id) for full content. Briefing responses shrink ~50% in typical use, well under Claude Code's response-size threshold.
• semantic_search tool trimmed — same shape, slightly more headroom (5 chunks × ~200 chars). New top-level hint field on the tool response.
• Shared trim helper — chunk-truncation logic lives in one place (semantic::trim_match), each caller passes its own budget.

What's automatic

• No env vars, no schema changes, drop-in.

From v0.7.2 to v0.7.3

What's new

• meta.time block on every /remember response — now_unix, now_utc, now_local, now_human, timezone, timezone_source, timezone_refresh_due. Per-user timezone cached server-side; refresh by passing client_timezone (IANA name) on any remember_* call.
• Actionable setup errors — settings_not_configured errors now carry an error.fix block with the exact MCP call to make.
• Per-call elapsed timing exposed in meta.elapsed_ms.

From v0.7.0 to v0.7.2

What's new

• Briefing latency cut — book-scoped semantic-search candidate filtering, parallel KB fetches, batched DB lookups. remember_briefing is significantly faster on instances with large embedded corpora.
• kb_semantic_matches reshape — now an envelope {enabled, reason, detail, results} so consumers can branch on enabled rather than guessing whether an empty list means opt-out vs. zero hits.
• Permission ACL filtering for semantic search — set bookstack_user_id in user settings to enable role-based filtering at the candidate-pool layer (much faster than the per-page HTTP fallback).
• Per-user identity auto-provisioning — first call to remember_user action=read creates the per-user Identity book + Identity page + journal-agent page if missing, returning what was created in auto_provisioned.
• Global org settings — admin-only org_identity_page_id, org_domains, org_required_instructions_page_ids, org_ai_usage_policy_page_ids shared across every user on the instance. First-write-wins for the structural IDs.
• Owner-only journal pages — auto-applied content permission lock so journal entries are visible only to the owning agent/user.

From v0.6.x to v0.7.0

What's new

• /remember protocol — server-side reconstitution + memory CRUD. 12 MCP tools: remember_briefing, remember_whoami, remember_user, remember_config, remember_identity, remember_directory, remember_journal, remember_collage, remember_shared_collage, remember_user_journal, remember_audit, remember_search. HTTP form: POST /remember/v1/{resource}/{action}.
• /settings UI — browser-based configuration page, token-gated via /authorize. Settings session cookie stored server-side (in-memory, 8h TTL).
• YAML-frontmatter provenance — every collection write stamps written_by, ai_identity_ouid, user_id, written_at, trace_id, resource, key, supersedes_page at the top of the page body. Invisible in BookStack's renderer; readable by tools.
• Soft delete — remember_*_collection action=delete prepends [archived] to the page name and stamps deleted: true in frontmatter rather than hard-deleting.
• remember_audit log — server-side audit table, scoped to the calling user, captures every write with trace_id and target_page_id.

From v0.5.3 to v0.6.x

What's new

• Image upload & file attachment tools — upload_image and upload_attachment accept either a staging_id (from prepare_upload), a public url, or BookStack's standard direct-upload form data.
• Staging upload flow — two-step prepare_upload → POST file to returned URL → call upload_image/upload_attachment with the staging ID. Lets containerized servers receive local files without exposing client paths. 5-minute TTL, single-use, 50MB cap.
• Move operations — dedicated move_page, move_chapter, move_book_to_shelf tools (cleaner than the implicit move via update operations).
• embed parameter on upload_image — auto-appends the uploaded image into the target page's content.
• DNS rebinding protection — reqwest client pins validated DNS addresses to prevent SSRF via DNS rebinding attacks.

From v0.5.2 to v0.5.3

v0.5.3 fixes embedding dimension detection, adds Ollama LLM support for summaries, and improves hybrid search scoring.

What's new

• Ollama LLM support — BSMCP_LLM_PROVIDER=ollama for instance summaries using local models (no API key needed)
• Configurable summary refresh — BSMCP_SUMMARY_INTERVAL (hours) for periodic regeneration instead of one-time only
• Configurable LLM base URL — BSMCP_LLM_API_URL for remote Ollama instances or custom endpoints
• Hybrid search scoring fix — keyword-only results no longer inflate above actual semantic matches via blanket boost. Pages with zero vector similarity are capped below real semantic results.
• Embedding dimension auto-detection fix — empty BSMCP_EMBED_DIMS env var no longer bypasses Ollama dimension detection (was silently defaulting to 768)
• Auto-reindex on dimension change — embedder now detects stored vs actual dimensions and triggers clean reindex automatically

What you must do

1. Pull new images: ghcr.io/bees-roadhouse/bsmcp-server:0.5.3 + ghcr.io/bees-roadhouse/bsmcp-embedder:0.5.3
2. Restart — dimension mismatch auto-reindexes if needed

From v0.5.1 to v0.5.2

v0.5.2 adds pluggable embedding providers, AI instance summaries, OAuth refresh tokens, and several quality-of-life improvements.

What's new

• Embedding providers — choose between local ONNX (local), Ollama (ollama), or OpenAI (openai) via BSMCP_EMBED_PROVIDER. Ollama auto-detects dimensions. OpenAI works with any compatible endpoint.
• AI instance summary — optional LLM call at startup generates a contextual summary of the knowledge base, included in MCP instructions so connecting AI assistants immediately understand what this BookStack is about. Supports OpenRouter, Anthropic, and OpenAI.
• OAuth refresh tokens — clients no longer need to re-enter API credentials every 24 hours. Refresh tokens silently issue new access tokens as long as BookStack credentials remain valid.
• Configurable token TTLs — BSMCP_ACCESS_TOKEN_TTL and BSMCP_REFRESH_TOKEN_TTL env vars.
• Job queue status page — /status now shows all pending/running jobs with progress bars plus recent completed/failed jobs.
• Similar-page computation — runs after every embedding job, not just full reindexes.
• WYSIWYG editing — all editing tools (edit_page, replace_section, append_to_page, insert_after) now explicitly documented to work on WYSIWYG pages.
• Duplicate title prevention — instructions tell AI not to include page title as H1 in content.
• Auto-migration fix — handles pre-semantic SQLite databases that lack pages table.

What's automatic

• All schema changes (refresh_tokens table, etc.) are applied on startup
• Existing deployments continue working with no env var changes
• Local ONNX embedding remains the default if BSMCP_EMBED_PROVIDER is not set

What you must do

1. Pull new images: ghcr.io/bees-roadhouse/bsmcp-server:0.5.2 + ghcr.io/bees-roadhouse/bsmcp-embedder:0.5.2 (or use latest)
2. Restart — that's it for the base upgrade

Optional: Enable AI instance summary — add LLM env vars:

BSMCP_LLM_PROVIDER=openrouter  # or: anthropic, openai, ollama
BSMCP_LLM_API_KEY=your-api-key  # not needed for ollama
BSMCP_SUMMARY_INTERVAL=24       # regenerate every 24h (0 = only on first startup)
# Uses BSMCP_EMBED_TOKEN_ID/SECRET for BookStack API access

Optional: Switch to Ollama/OpenAI embeddings — set BSMCP_EMBED_PROVIDER:

BSMCP_EMBED_PROVIDER=ollama
BSMCP_EMBED_MODEL=nomic-embed-text
BSMCP_EMBED_API_URL=http://ollama:11434

Switching provider triggers an automatic clean re-index.

From v0.5.0 to v0.5.1

v0.5.1 switches the default embedding model and adds automatic model change detection.

What's new

• Default model: EmbeddingGemma-300M — Google's lightweight embedding model (768 dims, 300M params). Faster and lighter than BGE-large, especially on ARM.
• Model change detection — embedder detects model changes via meta table and auto-triggers clean re-index with pgvector dimension adjustment
• Configurable embedding dimensions — pgvector column type automatically adjusts when switching models
• HuggingFace model downloads — custom ONNX models download automatically from HuggingFace Hub

What's automatic

• Full re-index — switching from BGE-large (1024 dims) to EmbeddingGemma (768 dims) triggers automatic clean re-index. PostgreSQL column type is altered automatically.
• No env var changes required unless you want to keep the old model

What you must do

1. Pull new images: ghcr.io/bees-roadhouse/bsmcp-server:0.5.1 + ghcr.io/bees-roadhouse/bsmcp-embedder:0.5.1
2. Restart — the embedder auto-detects the model change and re-indexes. Check progress at /status.
3. To keep the old model: Set BSMCP_EMBED_MODEL=BAAI/bge-large-en-v1.5 in your embedder env.

From v0.4.0 to v0.5.0

v0.5.0 is a search quality release — no infrastructure changes, just better results.

What's new

• Hybrid search — combines vector similarity with BookStack keyword search, weighted blend (70% vector + 20% keyword + blanket boost)
• Markov blanket re-ranking — pages whose graph neighbors also scored get a relevance boost (up to +0.15)
• Tighter chunking — max chunk size reduced from 2000 to 1200 chars with 150-char paragraph overlap between chunks
• Higher default threshold — raised from 0.50 to 0.65 to filter out low-quality matches
• Auto-reindex on upgrade — chunk version tracking triggers automatic clean re-index when chunking logic changes
• meta table — new key-value metadata table in both SQLite and PostgreSQL backends

What's automatic

• Full re-index — the embedder detects the chunk version change (v1 → v2) and automatically clears all embeddings and re-indexes everything on first startup. No manual reembed needed.
• Schema migration — meta table created automatically on startup
• All existing env vars and compose files are compatible

What you must do

1. Pull new images: ghcr.io/bees-roadhouse/bsmcp-server:0.5.0 + ghcr.io/bees-roadhouse/bsmcp-embedder:0.5.0
2. Restart — the embedder auto-detects the chunk version change and re-indexes. Check progress at /status.
3. No env var changes required — new hybrid parameter defaults to true in the semantic_search tool

New semantic_search parameters

Results now include a scoring breakdown when hybrid mode is on, showing vector, keyword, and blanket_boost components.

From v0.3.x to v0.4.0

v0.4.0 splits the monolithic bookstack-mcp container into separate server and embedder binaries with a pluggable database layer (SQLite or PostgreSQL + pgvector).

What's new

• Separate containers — bsmcp-server (MCP protocol, OAuth, search) and bsmcp-embedder (ONNX model, background embedding, /embed HTTP endpoint)
• PostgreSQL + pgvector — optional production backend with native HNSW vector indexing
• Database-backed job queue — embedding jobs persist across restarts
• Auto-migration — switch BSMCP_DB_BACKEND=postgres and the server migrates SQLite data automatically
• Dual MCP transport — SSE (2024-11-05) and Streamable HTTP (2025-03-26)
• New page editing tools — edit_page, append_to_page, replace_section, insert_after

What's automatic

• SQLite schema is compatible — same tables, same columns
• worker_id column auto-added to embed_jobs if missing
• Existing embeddings preserved (same model: BAAI/bge-large-en-v1.5, same 1024 dimensions)
• Auto-migration from SQLite to PostgreSQL when switching backends

What you must do

1. Replace compose file and images:

   • Old: single ghcr.io/bees-roadhouse/bookstack-mcp:latest container
   • New: ghcr.io/bees-roadhouse/bsmcp-server:latest + ghcr.io/bees-roadhouse/bsmcp-embedder:latest
   • Use docker/docker-compose.sqlite.yml (simple) or docker/docker-compose.yml (PostgreSQL)

2. Add new env vars:

   hljs language-bash

   Copy

   # Database backend (required)
   BSMCP_DB_BACKEND=sqlite   # or postgres
   
   # Embedder connection (required for semantic search)
   BSMCP_EMBEDDER_URL=http://bsmcp-embedder:8081
   
   # Separate BookStack API token for the embedder (required for semantic search)
   BSMCP_EMBED_TOKEN_ID=<BookStack API token ID>
   BSMCP_EMBED_TOKEN_SECRET=<BookStack API token secret>
   
   # PostgreSQL (only if switching to postgres)
   BSMCP_DATABASE_URL=postgres://bsmcp:yourpassword@bsmcp-postgres/bsmcp
   BSMCP_DB_PASSWORD=yourpassword
   

3. BSMCP_EMBED_THREADS is removed — use BSMCP_EMBED_CPUS (Docker CPU limit) instead.

4. Update webhook to use X-Webhook-Secret header instead of ?secret= query param (query param still works but is deprecated).

Migrating to PostgreSQL

Set BSMCP_DB_BACKEND=postgres and keep the SQLite file accessible at BSMCP_DB_PATH. The server auto-migrates all data on startup and renames the SQLite file to .db.migrated.

Manual migration is also available:

docker exec bsmcp-server bsmcp-server migrate \
  --from-sqlite /data/bookstack-mcp.db \
  --to-postgres postgres://bsmcp:yourpassword@bsmcp-postgres/bsmcp

Migration copies encrypted tokens as-is (portable when BSMCP_ENCRYPTION_KEY matches), converts embeddings from BLOB to pgvector format, and fixes PostgreSQL sequences.

From v0.1.x to v0.4.0

This is the largest jump — from a single monolithic container with no encryption and no semantic search to the full multi-container architecture.

What's automatic

• Plaintext tokens from v0.1.0-0.1.2 are transparently encrypted on first access (the server detects unencrypted values and re-encrypts them in place)
• All database tables are created on startup via CREATE TABLE IF NOT EXISTS

What you must do

1. Docker volume rename (v0.1.0-0.1.2 only — skip if already on v0.1.3+):

   hljs language-bash

   Copy

   docker compose down
   docker volume create bsmcp-data
   docker run --rm -v mcp-data:/source:ro -v bsmcp-data:/dest alpine cp -a /source/. /dest/
   docker volume rm mcp-data  # after verification
   

2. Update env vars:

   hljs language-bash

   Copy

   # REMOVE (no longer recognized):
   # BSMCP_PUBLIC_URL=https://mcp.example.com
   
   # ADD (required):
   BSMCP_ENCRYPTION_KEY=<generate: openssl rand -base64 48>
   BSMCP_PUBLIC_DOMAIN=mcp.example.com  # domain only, no https://
   
   # ADD (for semantic search):
   BSMCP_SEMANTIC_SEARCH=true
   BSMCP_WEBHOOK_SECRET=<random string, 16+ chars>
   BSMCP_EMBED_TOKEN_ID=<BookStack API token ID>
   BSMCP_EMBED_TOKEN_SECRET=<BookStack API token secret>
   BSMCP_EMBEDDER_URL=http://bsmcp-embedder:8081
   
   # ADD (for PostgreSQL — recommended):
   BSMCP_DB_BACKEND=postgres
   BSMCP_DATABASE_URL=postgres://bsmcp:yourpassword@bsmcp-postgres/bsmcp
   BSMCP_DB_PASSWORD=yourpassword
   

3. Replace compose file entirely:

   • Old: docker-compose.yml with ghcr.io/bees-roadhouse/bookstack-mcp:latest
   • New (SQLite): docker/docker-compose.sqlite.yml
   • New (PostgreSQL): docker/docker-compose.yml
   • Images: ghcr.io/bees-roadhouse/bsmcp-server:latest + ghcr.io/bees-roadhouse/bsmcp-embedder:latest

4. Create a BookStack API token for the embedder with read access to all content

5. Configure webhook in BookStack (see Semantic Search Setup)

6. Trigger initial embedding via the reembed MCP tool

From v0.1.2 to v0.1.3

See the v0.1.3 release notes:

• New required BSMCP_ENCRYPTION_KEY env var
• BSMCP_PUBLIC_URL renamed to BSMCP_PUBLIC_DOMAIN
• Docker volume renamed mcp-data to bsmcp-data
• PKCE enforcement for OAuth

Embedding Providers

Set via BSMCP_EMBED_PROVIDER. Changing provider or model triggers an automatic clean re-index.

Local (default)

Uses fastembed with ONNX Runtime. No external API needed but requires the heavier embedder container.

Ollama

Uses a local or remote Ollama instance. Dimensions auto-detected. No API key needed.

BSMCP_EMBED_PROVIDER=ollama
BSMCP_EMBED_MODEL=nomic-embed-text        # or any Ollama embedding model
BSMCP_EMBED_API_URL=http://ollama:11434    # default: http://localhost:11434

OpenAI

Uses OpenAI's embedding API or any OpenAI-compatible endpoint.

BSMCP_EMBED_PROVIDER=openai
BSMCP_EMBED_MODEL=text-embedding-3-small   # default
BSMCP_EMBED_API_KEY=sk-...
BSMCP_EMBED_DIMS=1536                      # must match model output
BSMCP_EMBED_API_URL=https://api.openai.com # or any compatible endpoint

Voyage

Uses Voyage AI's embedding API.

BSMCP_EMBED_PROVIDER=voyage
BSMCP_EMBED_MODEL=voyage-3-lite            # default
BSMCP_EMBED_API_KEY=pa-...
BSMCP_EMBED_DIMS=512                       # must match model output (voyage-3-lite = 512)
BSMCP_EMBED_API_URL=https://api.voyageai.com  # default

Reranker Providers

Set via BSMCP_RERANK_PROVIDER on the embedder. Off by default. See Reranker Setup for the activation walkthrough.

Local (default model: BAAI/bge-reranker-v2-m3)

In-process ONNX cross-encoder via fastembed. No external API needed. Downloads the model to BSMCP_MODEL_PATH on first run.

BSMCP_RERANK_PROVIDER=local
BSMCP_RERANK_MODEL=BAAI/bge-reranker-v2-m3   # default; reuses BSMCP_MODEL_PATH

Voyage

Uses Voyage AI's /v1/rerank endpoint.

BSMCP_RERANK_PROVIDER=voyage
BSMCP_RERANK_MODEL=rerank-2                # default
BSMCP_RERANK_API_KEY=pa-...
BSMCP_RERANK_API_URL=https://api.voyageai.com   # default

OpenAI-compatible (Voyage / Jina / Cohere via shim / self-hosted)

Any endpoint that accepts {model, query, documents} and returns {data:[{index, relevance_score}]} (or {results:[...]}). OpenAI itself has not shipped a rerank API — this provider is for compatible third parties. All three vars required (no upstream default).

BSMCP_RERANK_PROVIDER=openai
BSMCP_RERANK_MODEL=jina-reranker-v2-base-multilingual
BSMCP_RERANK_API_KEY=...
BSMCP_RERANK_API_URL=https://api.jina.ai

Search Operators

The search_content tool supports BookStack's search operators:

• "exact phrase" - Exact match
• {type:page} - Filter by type (page, chapter, book, shelf)
• {in_name:term} - Search within names only
• {created_by:me} - Filter by creator
• [tag_name=value] - Filter by tag

Uploading Local Files (Images & Attachments)

The MCP server runs in a container and cannot read files from the client machine's filesystem directly. To upload local images or file attachments, use the two-step staging upload flow:

Step 1: Call prepare_upload — returns a staging_id and a full upload_url:

{
  "staging_id": "f0103f6c-7c98-46c2-adbe-606ba26937c3",
  "upload_url": "https://your-mcp-host/stage/upload/f0103f6c-7c98-46c2-adbe-606ba26937c3",
  "ttl_seconds": 300
}

Step 2: POST the file to upload_url as multipart form-data. No auth header needed — the staging_id (a UUID that can only be generated via an authenticated MCP call) acts as the auth token for the one-time upload:

curl -X POST -F "file=@/path/to/image.jpg" \
  "https://your-mcp-host/stage/upload/f0103f6c-7c98-46c2-adbe-606ba26937c3"

Step 3: Call upload_image (or upload_attachment) with the staging_id:

{
  "name": "Banner Logo",
  "uploaded_to": 1908,
  "staging_id": "f0103f6c-7c98-46c2-adbe-606ba26937c3",
  "mime_type": "image/jpeg",
  "embed": true
}

The staging slot is consumed on first use (destructively removed from the store) and auto-expires after 5 minutes. Maximum file size is 50MB.

The embed parameter

upload_image accepts an embed boolean parameter (default false). When embed=true, the image is automatically appended to the target page's content after uploading, so you don't need a separate edit_page or append_to_page call. Works for both markdown and WYSIWYG pages.

Alternative: url parameter

If the file is already hosted at a public URL the MCP server can reach, you can skip the staging flow entirely and pass the url parameter directly to upload_image or upload_attachment. The server will fetch the file and forward it to BookStack.

Currently Claude Code only

The staging upload flow currently only works from Claude Code (the CLI tool). It does not work from Claude.ai's web custom connectors or Claude Desktop custom connectors.

The reason: Step 2 requires the MCP client to make an outbound HTTP POST to the MCP server's staging endpoint with the file bytes. Claude Code runs locally and has shell access (via its Bash tool), so it can curl the file directly. Claude.ai's remote MCP connector runs the MCP client inside Anthropic's sandboxed proxy infrastructure, which does not expose a mechanism for the client to make arbitrary HTTP file uploads to third-party hosts. Claude Desktop has similar limitations today.

If you're using Claude.ai or Claude Desktop, you can still use upload_image with the url parameter for files that are already web-accessible, or upload through the BookStack web UI directly.

Development

See DEVELOPMENT.md for build instructions, branching model, CI/CD (artifact-before-merge), versioning, and the workflow for adding new tools.

License

MIT