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Self-hosted MCP server for Obsidian: semantic + full-text search, wikilink graph, note CRUD, OAuth, and a self-describing vault guide.

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# Obsidian MCP Server

[![Python](https://img.shields.io/badge/python-3.12-3776AB?logo=python&logoColor=white)](https://www.python.org/)
[![License](https://img.shields.io/badge/license-MIT-green)](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/LICENSE)
[![MCP](https://img.shields.io/badge/MCP-compatible-7C3AED)](https://modelcontextprotocol.io)
[![PostgreSQL](https://img.shields.io/badge/PostgreSQL-16%20%2B%20pgvector-336791?logo=postgresql&logoColor=white)](https://www.postgresql.org/)

A self-hosted [Model Context Protocol](https://modelcontextprotocol.io)
server that turns your Obsidian vault into shared memory between you
and your AI agents. Indexed, searchable, and self-describing — agents
read what you read, link what you link, and pick up your folder
layout, frontmatter schema, and tag conventions on the first call
instead of being briefed from scratch every session.

Stack: Python 3.12, FastAPI, PostgreSQL with pgvector. Pluggable
embeddings (Ollama bge-m3, or OpenAI `text-embedding-3-{small,large}`).

![Dashboard](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/dashboard.png)

## Contents

- [Why this exists](#why-this-exists)
- [A session at the keyboard](#a-session-at-the-keyboard)
- [What's in the box](#whats-in-the-box)
- [vs. other Obsidian MCP servers](#vs-other-obsidian-mcp-servers)
- [Who this is for](#who-this-is-for)
- [Control panel](#control-panel)
- [Quick start](#quick-start)
- [Cost expectations](#cost-expectations)
- [The self-describing vault](#the-self-describing-vault)
- [Multi-user mode](#multi-user-mode)
- [Configuration](#configuration)
- [Architecture](#architecture)
- [Project layout](#project-layout)
- [Development](#development)
- [Security notes](#security-notes)

## Why this exists

There are three things going on here, and they're more interesting
together than apart.

### 1. A shared memory layer between you and your agents

I think of my Obsidian vault as my exocortex. The "big me" that
includes notes, calendars, scripts, search, and AI assistants is
substantially more capable than the "small me" of the biological brain
alone. It's also where I do most of my thinking, because writing
something down is itself a form of thought.

The problem is that until recently, the vault was passive. I had to go
find things. Agents that wanted to help me had to be briefed from
scratch every session, and they had no way to see what I'd already
written about a topic.

This server fixes that. Now the same vault feeds my own daily writing
and any agent I plug into it. The agent reads what I read, links what
I link, follows the same wikilinks, sees the same frontmatter. When I
write a project note on Sunday, my Monday-morning briefing agent
already knows about it. When the agent leaves notes from a research
session, they show up in my normal Obsidian search.

A concrete version of this: I'll spend a session in Claude Code on a
project, wrap up, push the commits, and then just say "update
Obsidian." The agent reads the vault guide, figures out where project
notes live in my structure, picks the right format and frontmatter,
and leaves a session log I can later roll into a status report. No
path-passing, no telling it what to write — the conventions are
already in the vault, and it follows them.

That's the exocortex idea made concrete: one place that holds
context, and both the human and the agents reading and writing into it
on the same terms.

### 2. Agent memory that you can actually read

The other half is the inverse. If you let an agent run for a while, it
needs memory. Most setups solve this with an opaque vector store, a
SQLite blob, or a managed "memory" service that you can't see into.
That works until you want to know what the agent thinks it knows about
you, or you need to correct something, or you want to understand why
it just made a weird suggestion.

This server gives you a different deal. Agent memory lives as markdown
files in your vault. Folder structure, file names, frontmatter, all
visible. You can open the file in Obsidian and read it. You can edit
it. You can delete it. You can grep it. The agent's "memory" is a
human-auditable artifact that sits in the same place as your own
notes, with the same tools available.

The home lab is the use case that sold me on this. My vault has notes
on the rack, the network, and every Home Assistant integration. I can
say "set up a night-light mode in the master bathroom, 1% after 11pm"
and a sysadmin agent finds the right config, makes the change, and
updates the doc in the same pass. Six months later when I've
forgotten how it works, the answer is in the vault, not buried in
some chat history I can't search.

The semantic search and wikilink graph still work over that material,
so retrieval is fast and conceptual. But the substrate is files you
own, not a black box.

### 3. The vault follows you

The thing that still surprises me is that this is internet-facing.
Same vault, same notes, reachable from anywhere I happen to be
talking to an agent — Claude in a browser tab, Claude on my phone on
the train, an n8n workflow firing on a schedule, a Claude Code
session on whatever laptop is in front of me. All behind OAuth or a
bearer key, all hitting the same source of truth.

So when I hear an interesting podcast on a commute and want to think
out loud about it with Claude on my phone, the agent isn't starting
cold. It can pull up what I've already written on adjacent topics,
surface a note I half-finished six months ago, and at the end of the
conversation suggest updates and write them in. The vault doesn't
have to be near me to be the thing I'm working in.

## A session at the keyboard

To make the abstract concrete, a short transcript from a real
session. Tool calls inline; the user types in plain English and the
agent picks the right tool.

```
you  > find anything I've written about why retrieval-augmented
       chat is hard

claude
  → semantic_search(query="why retrieval-augmented chat is hard")
    • 2025-12-03 - RAG failure modes.md
    • 2025-10-18 - Chunking tradeoffs.md
    • 2024-06-22 - Vector recall vs precision.md

the closest hit is your December note on RAG failure modes. let
  me see what it's connected to.

→ get_neighborhood(path="2025-12-03 - RAG failure modes.md",
                     depth=1)
    8 connected notes. notable ones:
    • "Hybrid search" — a re-ranking design you sketched but
      didn't ship
    • "Out-of-distribution queries" — a half-finished draft from
      October
    • "Embedding model choice" — links to bge-m3, your current
      model

you have three connected pieces here: a failure-modes catalog,
  an unshipped hybrid-search sketch, and an abandoned O-O-D draft.
  want me to summarize all three together, or pull up the draft
  so you can decide whether to finish it?

you  > pull up the draft, and tag it #revisit-2026 in frontmatter
       while you're there

claude
  → read_note(path="2025-10-18 - OOD queries (draft).md")
  → set_frontmatter(
      path="2025-10-18 - OOD queries (draft).md",
      updates={"tags": ["draft", "rag", "revisit-2026"]}
    )

here's the draft, and the tag is on. you stopped right before
  the section on confidence thresholds; the open question you
  left yourself was…
```

Two things to notice. First, the agent didn't need to be told what
folder to look in or what tools to use — it picked them. Second, the
write at the end is structured (`set_frontmatter` mutating YAML, not
a regex over the file body), so the note round-trips cleanly. The
self-describing vault and the wikilink graph are doing the work that
makes this feel natural.

## What's in the box

The server exposes 20 MCP tools across six concerns.

### Search and discovery
- `keyword_search(query, folder?, tags?, frontmatter?, limit=20)`,
  full-text via PostgreSQL `tsvector`; the text-search config(s) are
  configurable via `FTS_CONFIGS` (see
  [Full-text search language(s)](#full-text-search-languages))
- `semantic_search(query, folder?, tags?, frontmatter?, limit=15)`,
  vector similarity via pgvector, one preview chunk per note
- `list_notes(folder?, limit=50)`, sorted by modified time
- `get_recent(folder?, limit=20)`, recently changed
- `get_tags(limit=50)`, tag and count
- `get_vault_guide()`, the Obsidian primer plus this vault's
  `CLAUDE.md`, served live

### Read and write
- `read_note(path, section?, offset=0, limit?)`, bounded by
  `MAX_READ_RESPONSE_CHARS` (default 40,000) — see
  [Response size limits](#response-size-limits). `section=<heading>`
  returns one section instead of the whole note; `offset` continues a
  truncated read.
- `create_note(path, content)`, atomic write, refuses overwrite
- `edit_note(path, …)` with four mutually exclusive modes: full
  replace (default), `append=True`, `find=…` (with optional
  `replace_all`), or `section=<heading>` (ATX headings, supports
  `Parent/Child` path-style and `#N` ordinal disambiguation).
  `dry_run=True` returns a unified diff without writing. Legacy clients
  may use `operation="append"`; `operation="replace"` explicitly selects
  full replace.
- `move_note(from_path, to_path, rewrite_links=False)`, relocates and
  optionally rewrites incoming `[[Old]]`, `[[Old|alias]]`,
  `[[Old#anchor]]`, `![[Old]]`, and `[[folder/Old]]` references in
  source notes
- `delete_note(path, permanent=False)`, soft-delete to
  `.trash/<YYYYMMDD-HHMMSS>-<basename>` by default. `permanent=True`
  does a hard `os.unlink`.
- `set_frontmatter(path, updates, remove?)`, structured YAML
  mutation. Body is byte-identical when only frontmatter changes.

### File access (non-markdown)
Raw read/write/browse of arbitrary vault files (PDFs, images, skill
assets, data files) — distinct peers to the note tools, which stay
markdown-only. Pure byte transport: no server-side PDF/text extraction,
no embedding or indexing of non-markdown files.
- `read_file(path, encoding="auto", offset=0, limit?)`, returns
  text-like files as text, images as an inline image block that renders
  in-client, and other binaries as a base64 string. `text`/`base64`
  force the form. Refuses files over `MAX_FILE_READ_BYTES` (default
  10 MB); text results are additionally bounded by
  `MAX_READ_RESPONSE_CHARS` and continue via `offset`.
- `write_file(path, content, encoding="base64", overwrite=False)`,
  lands a file in the vault; base64 for binary, `text` for UTF-8.
  No-clobber by default, auto-creates parent dirs, atomic write.
  Capped at `MAX_FILE_WRITE_BYTES` (default 25 MB).
- `list_files(folder=".", pattern="*", recursive=False, limit=200)`,
  `ls`-style browse of files and subdirectories with size and mtime,
  glob-filterable and result-capped.
- `delete_file(path, permanent=False)`, soft-deletes a non-markdown
  file to `.trash/<YYYYMMDD-HHMMSS>-<basename>-<8 hex>` with a single
  atomic rename. Refuses markdown (that is `delete_note`), directories,
  and symlinks.

All four reuse the path-traversal guard and exclude dot-directories
(`.obsidian`, `.git`, `.trash`, …), matching the indexer's visibility
rule.

### File transfer
No MCP client can hand a tool the bytes of a file the user is looking
at, so `write_file` is only usable when the agent already has the
content. These tools close that gap with short-lived capability links,
redeemed over the public `/transfer/*` routes.
- `request_upload(path, overwrite=False, expires_in?)`, mints a
  single-use link bound to exactly one destination path. The human
  opens it, picks a file, and it lands at `path` — nothing else can be
  written with it.
- `check_upload(upload_id)`, reports `pending` / `uploading` /
  `completed` (with size, sha256 and MIME) / `expired`, scoped to the
  identity that minted it.
- `request_download(path, expires_in?)`, mints a link the human can
  save one vault file from. Usable more than once until it expires, and
  bound to the file's exact bytes at mint time.
- `import_from_url(url, path, overwrite=False)`, fetches a public https
  asset straight into the vault under an explicit outbound deny policy
  (no private, loopback, link-local, metadata or tunnelled addresses,
  in any spelling, re-checked at every redirect).

The token travels in the URL *fragment*, which browsers never send, so
no server-generated request target or access log contains it. Uploads
are claimed before a body byte is read, published atomically with
no-clobber semantics, and bound at mint time to the file state they
were minted against — a link cannot silently undo an edit made while it
was waiting. `MCP_HOSTNAME` or `BASE_URL` must be set; without a public
origin the mint tools refuse rather than emit a localhost link.

### Wikilink graph
- `get_backlinks(path, limit=50)`, notes linking TO `path`
- `get_links(path)`, outgoing links, both resolved and dangling
- `get_neighborhood(path, depth=1, limit=50)`, undirected BFS over the
  resolved-link graph, capped at depth ≤ 5 and limit ≤ 200
- `find_related(path, limit=10)`, semantic neighbors via averaged
  chunk embeddings and pgvector cosine distance, deduped per note
- `find_orphans(folder?, limit=50)`, notes with zero in or out
  resolved links

### Auth and ops
- API keys with the `omcp_` prefix, stored as SHA-256 hashes, with
  `read` and `readwrite` permission scopes. Write tools refuse on
  read-only keys.
- OAuth 2.0 PKCE (S256) flow for public and confidential clients,
  including ChatGPT, Claude Desktop, and claude.ai. Dynamic registration
  defaults to both vault permission levels; the user chooses the actual
  grant on the consent screen.
- Control panel (Jinja2, htmx, Tailwind) for keys, usage logs,
  indexer status, embedding-provider info, and a danger-zone reset.
- Every tool call is logged to `usage_logs` with name, params
  (truncated to 200 chars), duration, and response size.

All write tools route through `src/services/vault.py::write_file`,
which writes to a tmp file in the same directory and `os.replace()`s
it onto the destination. A crash mid-write cannot truncate a note.

## vs. other Obsidian MCP servers

There are several existing MCP servers for Obsidian, and most of them
solve a different problem than this one. The lightweight ones are
glue over Obsidian's Local REST API plugin or the filesystem: they
let an agent reach the files, but don't build any infrastructure of
their own. They're great if "I just want Claude to read my notes"
is the goal and you keep Obsidian running locally.

This server is on the other end of the spectrum: a real backend with
a persistent index, semantic retrieval, a wikilink graph, OAuth, and
an admin UI. The cost is Postgres and Docker. The benefit is
everything you can build on top of that.

|  | This server | [MarkusPfundstein/mcp-obsidian][mp] | [StevenStavrakis/obsidian-mcp][sg] | [jacksteamdev/obsidian-mcp-tools][js] |
| --- | --- | --- | --- | --- |
| Persistent index (Postgres) | ✅ | — | — | — |
| Semantic search (vectors) | ✅ | — | — | — |
| Wikilink graph queries | ✅ | — | — | partial |
| Runs without Obsidian open | ✅ | — | ✅ | — |
| OAuth 2.0 client flow | ✅ | — | — | — |
| Multi-user / per-user vaults | ✅ | — | — | — |
| Admin UI + usage logs | ✅ | — | — | — |
| Atomic writes + dry-run diffs | ✅ | — | — | — |
| Setup tax | Postgres + Docker | Obsidian + REST plugin | Python only | Obsidian plugin |

[mp]: https://github.com/MarkusPfundstein/mcp-obsidian
[sg]: https://github.com/StevenStavrakis/obsidian-mcp
[js]: https://github.com/jacksteamdev/obsidian-mcp-tools

Comparison reflects each project's documented features at time of
writing; verify the specifics before betting on them.

## Who this is for

- Homelab folks who already run Postgres and Docker, or are happy
  to spin them up. The setup tax is the price of admission for the
  semantic and graph layers.
- People who keep an opinionated vault — task placement logic,
  frontmatter schemas, tag taxonomy — and want agents to follow
  those conventions on the first call instead of being briefed
  every session.
- Anyone running more than one MCP client (Claude Desktop, Claude
  Code, Claude in a browser, n8n) against the same notes and tired
  of re-explaining the vault to each.
- Folks who want agent memory to live as plain markdown files they
  can read, edit, grep, and version-control, not in an opaque
  vector store or a managed memory service.

### Who this isn't for

- "I just want Claude to read my notes" with the lightest possible
  setup. Use one of the filesystem-glue projects above; you don't
  need this.
- Anyone unwilling to run a database. There is no SQLite fallback;
  pgvector is doing real work, and a managed Postgres with
  pgvector support is part of the stack.
- People who want a turnkey hosted product. This is a self-hosted
  server you run yourself.

## Control panel

The server ships with a built-in admin UI for the parts of operations
that are easier to look at than to query: minting keys, watching the
indexer, eyeballing tool-call traffic, and resetting embeddings when
you switch providers.

### Usage

Per-tool-call audit log with a 14-day request histogram. Every MCP
call is recorded with the calling key, tool name, duration, and
response size — useful for noticing a misbehaving agent burning
tokens on something it shouldn't.

![Usage](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/usage.png)

### API keys and OAuth clients

Bearer keys with `read` / `readwrite` scopes for API clients, and a
separate OAuth 2.0 PKCE flow for clients like ChatGPT, Claude Desktop,
and claude.ai that expect a proper authorization-code dance. The OAuth
server supports public (`none`) and confidential (`client_secret_post`)
token-endpoint authentication plus refresh tokens.

![API keys](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/api-keys.png)
![OAuth clients](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/oauth-clients.png)

### Vault browser

A read-only file tree of the mounted vault, mostly for sanity-checking
that the container sees what you think it sees.

![Vault](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/vault.png)

### Settings

Indexer status, current embedding provider and model, vault path, and
the danger-zone reset that drops and recreates the embeddings column
at the configured dimension. Use this when switching providers.

![Settings](https://raw.githubusercontent.com/maxkuminov/obsidian-mcp/main/screenshots/settings.png)

## Quick start

> Deploying on a VPS from scratch? See [`DEPLOYMENT.md`](./DEPLOYMENT.md)
> for the full walkthrough: Postgres setup, Caddy and TLS, vault sync
> via Nextcloud, and the gotchas that bite first-time deploys.

The bundled Caddy configuration fails closed on `/admin`, `/api`, and
`/authorize`; replace its placeholder basic-auth hash before starting it.

### Prerequisites

- Docker and Docker Compose
- A PostgreSQL 16 instance reachable from the container, with the
  `pgvector` extension installed
- Either an Ollama instance running `bge-m3`, or an OpenAI API key.
  Anything that speaks the OpenAI embeddings protocol works (Azure
  OpenAI, OpenRouter, Together, etc.).

### 1. Clone, configure, point at your vault

```bash
git clone https://github.com/maxkuminov/obsidian-mcp.git
cd obsidian-mcp
cp .env.example .env
$EDITOR .env
```

In `docker-compose.yml`, point the `/obsidian` volume at your vault:

```yaml
volumes:
  - /path/to/your/vault:/obsidian
```

### 2. Pick an embedding backend

Option A, OpenAI (zero local infra):

```env
EMBEDDING_PROVIDER=openai
OPENAI_API_KEY=sk-...
EMBEDDING_DIMENSIONS=1024
OPENAI_EMBEDDING_MODEL=text-embedding-3-small
```

The server validates `OPENAI_API_KEY` at startup and refuses to boot
if it's missing.

Option B, Ollama (self-hosted, GPU recommended):

```env
EMBEDDING_PROVIDER=ollama
OLLAMA_URL=http://your-ollama-host:11434
EMBEDDING_MODEL=bge-m3
EMBEDDING_DIMENSIONS=1024
```

This is the default. Omitting `EMBEDDING_PROVIDER` falls back to
Ollama.

### 3. Deploy

```bash
make init       # data dirs and .env from template (skip if you've already edited)
make db-init    # create database, user, and pgvector extension
make deploy     # build, push to local registry, run migrations, recreate container
```

The first deploy backfills the index, the wikilink graph, and the
embeddings. For a 2 to 3k-note vault on Ollama with a GPU this takes
a few minutes. On `text-embedding-3-small` it's seconds.

### 4. Connect a client

Mint an API key in the control panel, then point your MCP client at:

```
URL:  https://obsidian-mcp.<your-domain>/mcp
Auth: Bearer omcp_...
```

For Claude Desktop, add to `claude_desktop_config.json`:

```json
{
  "mcpServers": {
    "obsidian": {
      "url": "https://obsidian-mcp.<your-domain>/mcp",
      "headers": { "Authorization": "Bearer omcp_..." }
    }
  }
}
```

For Claude Code:

```bash
claude mcp add obsidian --transport http \
  --url "https://obsidian-mcp.<your-domain>/mcp" \
  --header "Authorization: Bearer omcp_..."
```

The first thing any agent should do in a new session is call
`get_vault_guide()`. That's how it learns your folder structure,
naming conventions, and YAML schema before it writes anything.

## Cost expectations

If you go the OpenAI route (the realistic path on a CPU-only VPS),
the first-index spend is small and the steady state is nearly free.
Rough numbers assuming an average note around 1,500 tokens (three
512-token chunks), at OpenAI's published rate at time of writing:

| Model | $/1M tokens | 1k notes | 10k notes | 100k notes |
| --- | --- | --- | --- | --- |
| `text-embedding-3-small` | $0.02 | ~$0.05 | ~$0.50 | ~$5.00 |
| `text-embedding-3-large` | $0.13 | ~$0.30 | ~$3.00 | ~$30.00 |

After the first index, only changed notes are re-embedded. Ongoing
cost is proportional to edits — pennies a month for a typical vault.

If you self-host Ollama with a GPU, embedding cost is whatever your
power bill is. Ollama on CPU works but is too slow to be usable on
a vault of more than a few hundred notes.

## The self-describing vault

This is the part most "MCP for Obsidian" projects miss. They stop at
read, write, and list. The interesting question isn't "can the agent
reach the files," it's "does the agent know the rules?"

If you have an opinionated vault — task placement logic, folder
conventions, required frontmatter, tag taxonomy — an agent with write
access can do real damage without that context. Tasks land in the
wrong folder. Bare-date filenames collide with templates. Wrong tags
break Dataview queries. The data layer works fine; the context layer
is where the failures show up.

The fix is small. Keep a machine-readable instruction file
(`CLAUDE.md` at the vault root) that describes the system's own rules.
Expose it as a dedicated tool. Every connecting agent calls it once at
the start of a session and immediately knows how the vault works.
Update the file, every agent sees the change on the next call. No
client-side config. No system-prompt injection. The vault is
authoritative about its own rules.

`get_vault_guide()` does exactly this. It returns a generic Obsidian
primer (wikilink syntax, embed syntax, tag conventions, common plugin
literals) plus the vault's `CLAUDE.md` live. The hint to call it first
is baked into the write-tool descriptions so the agent gets pulled
into the right behavior even without prompting.

## Multi-user mode

Single-user mode is the default and works exactly as described above —
one vault, one set of API keys, no in-app user concept. Multi-user mode
is an opt-in flag that turns the same container into a small
multi-tenant deployment: in-app username/password login, per-user vault
scoping, an admin role for troubleshooting, and a regular-user role
that sees only its own keys/OAuth clients/usage. One container, one
Postgres, strict isolation between users.

Enable it on an existing deployment with no data loss — your current
vault and keys carry over to the bootstrap admin.

### Enabling

1. Set `MULTI_USER_MODE=true` and a strong `SECRET_KEY` in `.env`
   (`openssl rand -hex 32` is fine). The app refuses to start with the
   placeholder value when the flag is on.
2. `make deploy` (or `docker compose up -d --force-recreate`).
3. Visit the panel. Because the `users` table is empty, you're routed
   to `/admin/register` — the one-time bootstrap form. It's still
   behind Traefik's `chain-oauth@file` middleware, so only people
   Traefik already trusts can claim admin.
4. Register with a chosen username and password. The bootstrap form
   pre-fills `vault_path` with whatever `VAULT_PATH` was set to, so
   your existing notes immediately belong to this new admin. No
   re-index, no re-embed, no data loss — every previously indexed
   note, API key, OAuth client, and usage log row gets backfilled
   to the bootstrap user in a single transaction.

### Inviting users

1. Edit `docker-compose.yml` to add a volume mount for the new user's
   vault under `/vaults/<username>`. Host paths with spaces must be
   quoted as a single YAML string:

```yaml
   volumes:
     - "/storage/vaults/alice:/vaults/alice"
     - "/storage/shared/bob/Obsidian:/vaults/bob"
   ```

`make deploy` to apply.
2. In the panel, `/admin/users/create` — pick a username and set an
   initial password.
3. `/admin/users/{id}/edit` — set the user's `vault_path` to the
   container path you just mounted (e.g. `/vaults/bob`). The form
   shows a dropdown of unassigned `/vaults/*` directories that exist
   on disk.
4. Share the credentials out-of-band. The user logs in at
   `/admin/auth/login`, gets their own keys/OAuth/usage views, and
   cannot see other users' notes.

### What admins see

Admins see API keys, OAuth clients, and usage logs for all users; they
own the Settings page (embedding provider, indexer trigger, danger
zone) and the Users page. Admins do **not** browse other users' vault
contents through the panel — that's intentional. Troubleshooting
another user's vault means either inspecting it via `docker exec` or
temporarily reassigning their `vault_path`, not UI snooping.

### Rolling back

Set `MULTI_USER_MODE=false`, restart. Existing API keys keep working
(per-user filters skip when no user context is set), the login UI and
session cookies disappear, and the panel falls back to its
Traefik-OAuth-only mode. The schema stays in place, so flipping back
to multi-user later resumes where you left off without re-bootstrapping
(the `users` table is non-empty, so `/admin/register` is closed).

### Constraints and known limits

- The indexer iterates active users sequentially each cycle. Fine for
  tens of users; hundreds would need parallelization.
- Password reset is admin-driven only — there's no email-based
  self-service flow.
- No rate limiting on `/admin/auth/login`. The Traefik OAuth gate in
  front of the panel is the main brute-force defense; if you expose
  `/admin/auth/login` to the open internet, put a rate-limit
  middleware in front of it.
- The `vault_path` validator does not resolve symlinks, so an admin
  can technically point a user at host files via a symlinked
  `/vaults/<name>`. Treat `/vaults/` as an admin-trust boundary.

## Configuration

| Variable | Default | Purpose |
| --- | --- | --- |
| `DATABASE_URL` | — | `postgresql+asyncpg://user:pass@host/db` |
| `VAULT_PATH` | `/obsidian` | In-container vault mount |
| `SECRET_KEY` | — | itsdangerous signer key |
| `INDEX_INTERVAL_SECONDS` | `300` | Periodic reindex cadence |
| `MAX_FILE_READ_BYTES` | `10485760` | `read_file` cap (10 MB); bounds what the server reads from disk |
| `MAX_FILE_WRITE_BYTES` | `26214400` | `write_file` cap (25 MB), decoded byte length |
| `MAX_READ_RESPONSE_CHARS` | `40000` | `read_note` / `read_file` cap on what is returned to the caller (≈10K tokens). See [Response size limits](#response-size-limits). |
| `FTS_CONFIGS` | `english` | Keyword-search text-search config(s). JSON or CSV. See [Full-text search language(s)](#full-text-search-languages). |
| `TRANSFER_TOKEN_TTL_SECONDS` | `600` | Default life of a transfer link. Per-call `expires_in` is clamped to 60–3600. |
| `TRANSFER_MAX_UPLOAD_SECONDS` | `600` | How long one claimed upload may stream before the token is spent |
| `TRANSFER_MAX_CONCURRENT_UPLOADS` | `4` | Simultaneous upload streams |
| `IMPORT_ALLOW_HTTP` | `false` | Let `import_from_url` fetch plain http. Off by default. |
| `EMBEDDING_PROVIDER` | `ollama` | `ollama` or `openai` |
| `EMBEDDING_DIMENSIONS` | `1024` | pgvector column width |
| `OLLAMA_URL` | — | Used when provider is Ollama |
| `EMBEDDING_MODEL` | `bge-m3` | Ollama model name |
| `OPENAI_API_KEY` | — | Required when provider is OpenAI |
| `OPENAI_BASE_URL` | `https://api.openai.com/v1` | Override for Azure or proxies |
| `OPENAI_EMBEDDING_MODEL` | `text-embedding-3-small` | OpenAI model |
| `CHUNK_SIZE` | `512` | Approx tokens per chunk (4-char heuristic) |
| `CHUNK_OVERLAP` | `0` | Token overlap between chunks |
| `MCP_SANDBOX_MODE` | `false` | Registry-eval only. Skips DB, indexer, embedding provider, and `/mcp` auth so introspection works without external deps. Do not enable in production. |

See `.env.example` for the full set with comments. For first-index
spend on OpenAI, see [Cost expectations](#cost-expectations) above.

### Switching providers

Different models produce non-comparable vectors, so a provider switch
requires reindexing.

1. `make down`
2. Update `.env`. Change `EMBEDDING_PROVIDER`, set credentials,
   optionally change `EMBEDDING_DIMENSIONS`.
3. `make reset-embeddings`
4. `make up`. The next indexer pass re-embeds the vault.

You can also use Settings → Danger zone → Reset embeddings in the
control panel, which performs the same SQL while the server is running
(pauses the indexer, runs the SQL, resumes).

If you change `EMBEDDING_DIMENSIONS` without running the reset, the
server detects the mismatch at startup and exits non-zero with a
pointer to the reset target.

### Full-text search language(s)

`keyword_search` runs over a PostgreSQL `tsvector`. The *text-search
configuration* it uses — the stemmer and stop-word dictionary — is
controlled by `FTS_CONFIGS`. It defaults to `english`, which reproduces
the historical behavior exactly, so existing deployments need no action.

`FTS_CONFIGS` is a **list**, settable as JSON
(`FTS_CONFIGS=["simple","norwegian"]`) or comma-separated
(`FTS_CONFIGS=simple,norwegian`). Each note is indexed under *every*
listed config, and a query matches if *any* listed config's parse hits.
This is what makes a mixed-language vault work:

| `FTS_CONFIGS` | Behavior |
| --- | --- |
| `english` | English Snowball stemmer (default; `running` ↔ `run`). |
| `simple` | Language-agnostic. No stemming or stop-words — matches exact word *forms*. A principled default for mixed-language vaults: keyword search is the exact-match arm, while `semantic_search` (bge-m3 is multilingual) handles morphological recall. |
| `english,norwegian` | Both stemmers applied — keyword-side morphology for two languages at once. |
| `simple,norwegian` | Verbatim lexemes **plus** Norwegian stems. |

The setting is **global** — applied to every vault (consistent with
`EMBEDDING_MODEL`, `CHUNK_SIZE`, etc., which are global too). For a
mixed-language multi-user instance, set a superset (e.g.
`["english","norwegian"]`, or `["simple"]`). Per-user FTS config is a
clean future extension but is not implemented.

A typo'd or uninstalled config name fails fast at startup with a message
listing the configs available in your Postgres instance, rather than
producing silent zero-result searches.

**Changing `FTS_CONFIGS` requires a rebuild.** Stored tsvectors are
computed at index time, so they go stale when the config list changes.
After editing `.env` and redeploying, run:

```
make rebuild-tsvectors
```

This re-reads each note and recomputes its `content_tsvector` under the
new config(s). It rebuilds the **keyword index only** — it does **not**
touch embeddings/vectors and makes **no API calls**, so it finishes in
seconds for a few thousand notes. (Do not confuse it with the expensive
`make reset-embeddings` flow.)

> **Tokenization caveat:** the tsvector *parser* still splits on
> punctuation and hyphens regardless of config, so `bge-m3` tokenizes to
> `bge` + `m3`. `simple` preserves word *forms*, not punctuation-bearing
> strings; exact-string-with-punctuation matching would need a trigram
> index and is out of scope.

### Response size limits

A tool result is model input. Whatever `read_note` returns is fed
straight back into the caller's next request, so an unbounded read is
an unbounded prompt — and the caller usually finds out only when its
inference provider rejects the request.

`MAX_READ_RESPONSE_CHARS` (default 40,000, roughly 10K tokens) bounds
what `read_note` and the text results of `read_file` return. It is a
**different limit** from `MAX_FILE_READ_BYTES`, which bounds what the
server reads off disk. A 3 MB note is comfortably within the 10 MB read
cap and will still destroy a context window; both caps are needed and
they have different correct values.

It applies **per component**, not once to the whole response: the
content window gets the cap, and the heading outline gets it
independently. A truncated read can carry both, so budget for a
worst case of roughly `2 × MAX_READ_RESPONSE_CHARS` plus a few hundred
characters of fixed notice text — not one cap's worth.

When a note exceeds the cap you get the first window plus a
`[TRUNCATED]` notice carrying the exact `offset` to continue from, and
— for a whole-note read — an outline of the note's sections:

```
- `#1` `# Client Records` (2,855,343 chars)  ⚠ over the cap, will page
- `#2` `## Balance Sheet.xlsx` (391,199 chars)  ⚠ over the cap, will page
- `#3` `## Lease Agreement.pdf` (464 chars)
- `#4` `## Invoice 2025-044.pdf` (1,075 chars)  ← duplicate title, use the ordinal
```

Paging a multi-megabyte note 40K at a time is technically possible and
practically useless, so prefer the outline: read the one section you
want with `read_note(path, section="Lease Agreement.pdf")`. Sections are
addressable three ways — the `#N` ordinal shown in the outline, the
`Parent/Child` path-style form, and exact heading text. The ordinal is
the only form that separates **duplicate sibling** headings, which share
every ancestor and so cannot be disambiguated by path; notes generated
by bulk extraction tend to be full of them.

A bare `#N` **always** selects by position, so an ordinal we hand you in
an outline can never be shadowed by a heading that happens to be titled
`#2`. Such a heading stays reachable via the path form (`Parent/#2`) or
via its own ordinal.

The outline is itself bounded by the cap: a note with thousands of
headings gets a truncated listing that reports how many sections were
omitted and the full ordinal range, rather than an outline larger than
the content window it accompanies.

`limit` can lower the cap for a single call but never raise it. If your
clients genuinely want larger reads, raise `MAX_READ_RESPONSE_CHARS` —
that is an operator decision, made once, by someone who knows the
deployment.

> **Upgrading:** this is a visible contract change. Before this, a
> `read_note` on a large note returned the whole thing; now it
> truncates. The notice is self-describing, so an agent needs no prior
> knowledge to continue, but a script that assumed whole-note reads
> should either pass `section=` or raise the cap.

## Architecture

```
┌──────────────┐                       ┌──────────────────────┐
│ MCP clients  │   HTTP + Bearer key   │   FastAPI app        │
│  Claude Desk │ ────────────────────▶ │  ┌────────────────┐  │
│  Claude Code │                       │  │  MCP server    │  │
│  n8n agents  │                       │  │  (20 tools)    │  │
│  OpenWebUI   │                       │  └─────┬──────────┘  │
└──────────────┘                       │        ▼             │
                                       │  ┌────────────────┐  │
                                       │  │  Services:     │  │
                                       │  │  - vault       │  │
                                       │  │  - search      │  │
                                       │  │  - embeddings  │  │
                                       │  │  - links       │  │
                                       │  │  - indexer     │  │
                                       │  └─────┬──────────┘  │
                                       │        ▼             │
                                       │  ┌────────────────┐  │
                                       │  │ Postgres +     │  │
                                       │  │ pgvector       │  │
                                       │  └────────────────┘  │
                                       └──────────┬───────────┘
                                                  ▼
                                       ┌────────────────────┐
                                       │  Embedding         │
                                       │  provider          │
                                       │  (Ollama / OpenAI) │
                                       └────────────────────┘
```

### Indexing pipeline

```
.md files in vault
    ↓ skip dot-dirs
parse frontmatter, extract tags (YAML + inline #hashtags)
    ↓ SHA-256 hash
skip if unchanged
    ↓
UPSERT notes_metadata (path, title, tags[], frontmatter JSONB,
                       content_hash, tsvector, modified_at)
    ↓
extract wikilinks/embeds/markdown-links → resolve targets →
note_links (source_id, target_id or NULL for dangling)
    ↓
chunk content (512 tokens, no overlap) → embed via provider →
note_embeddings (note_id, chunk_index, chunk_text, embedding[N])
    ↓
set embedded_content_hash = content_hash
```

The indexer runs on startup and every `INDEX_INTERVAL_SECONDS` (5
minutes by default). Hashes are content-only, so the change detector
ignores mtime jitter. Stale embeddings are caught by the
`embedded_content_hash != content_hash` mismatch.

### Database schema

| Table | Purpose |
| --- | --- |
| `notes_metadata` | Path, title, tags, frontmatter, content hash, embedded hash, tsvector, modified time |
| `note_embeddings` | One row per chunk. `embedding` is `vector(EMBEDDING_DIMENSIONS)`. |
| `note_links` | Wikilink graph: source/target IDs, target_path, kind (`link`, `embed`, `markdown`) |
| `api_keys` | Hashed bearer tokens, prefix for display, permission, expiry |
| `usage_logs` | Per-tool-call audit |
| `oauth_clients`, `oauth_codes`, `oauth_tokens` | OAuth 2.0 PKCE state |

GIN indexes on `content_tsvector` and `tags[]`. B-tree indexes on the
hot foreign keys. pgvector HNSW index on the embedding column
(`vector_cosine_ops`, `m=16, ef_construction=64`); queries set
`hnsw.ef_search=80` and dedupe per note in Python after a 5x overfetch.

## Project layout

```
src/
  main.py             FastAPI app, lifespan, MCP mount
  config.py           pydantic-settings
  database.py         async SQLAlchemy engine/session
  models/db.py        ORM models
  mcp_server/         MCP server, tools, auth middleware
  services/           vault ops, search, embeddings, links, indexer
  api/                control-panel REST endpoints
  control_panel/      Jinja2 templates and static assets
  oauth/              OAuth 2.0 authorization-code flow
alembic/              database migrations
scripts/              one-off ops scripts (e.g. reset_embeddings.py)
tests/                pytest suite + smoke-test docs
openspec/             change proposals (spec-driven workflow)
```

## Development

```bash
pip install -r requirements-dev.txt
pytest
```

The unit-test suite covers the embedding-provider abstraction, OpenAI
batching and retry behavior, config validation, and the
dimension-mismatch startup check. Network-bound tests use `respx` to
mock httpx, so no real network access is required.

To run the server outside Docker:

```bash
DATABASE_URL=... SECRET_KEY=... VAULT_PATH=... uvicorn src.main:app --reload
```

## Make targets

```
make init             First-time setup (data dirs, .env)
make build            Build Docker image (no cache)
make deploy           Build, scan, push, backup, migrate, recreate container
make db-init          Create database, user, and pgvector extension
make db-migrate       Run alembic migrations
make db-backup        Dump database to backups dir
make logs             Tail container logs
make reindex          Trigger a reindex via the API
make reset-embeddings Drop and recreate embedding column at configured dim
make rebuild-tsvectors Recompute keyword index for FTS_CONFIGS (no embeddings, no API calls)
make status           Show container and health status
```

## Security notes

- API keys use the `omcp_` prefix and are stored as SHA-256 hashes.
  The raw key is shown exactly once at creation.
- The control panel is intended to sit behind an external auth
  gateway. The included `docker-compose.yml` uses Traefik with an
  OAuth chain. Don't expose `/admin` directly to the internet.
- The OpenAI key is rendered on the settings page as
  `key[:8] + "..." + key[-4:]` and never appears in full in HTML or
  JS sources.
- Path traversal is blocked at the service layer. All write paths
  resolve through `Path.resolve().relative_to(vault_root)`.
- Parameterized queries everywhere. No string interpolation into SQL.
- Response headers include HSTS, `X-Content-Type-Options: nosniff`,
  and `X-Frame-Options: DENY`.

## Status

Single-author, in active use as the maintainer's personal exocortex
(2,500+ notes, multiple connecting agents). Public for anyone who
wants to fork it. Issues and PRs welcome but expect opinionated review.
This is a working system, not a generic platform.

## License

MIT. See `LICENSE`.

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