If you keep most of your notes in Bear, at some point you may want a small AI assistant that can help you search through them, summarize related ideas, or prepare updates without opening dozens of notes manually.

That sounds simple until you reach the important part: your notes are private, local, and often messy. You probably do not want a generic chatbot freely editing, tagging, archiving, or deleting them.

The safer approach is to create a dedicated AI agent with one job: manage Bear notes carefully through Bear’s official command-line interface.

In my case, the goal was to connect LibreChat with Bear’s official MCP server and expose it as a separate “Bear Notes” agent.

What we are building

The final setup looks like this:

LibreChat
   ↓
Bear Notes Agent
   ↓
Bear MCP server
   ↓
bearcli
   ↓
Bear notes on your Mac

If LibreChat runs directly on macOS, the setup is fairly direct. LibreChat can start Bear’s MCP server through bearcli.

If LibreChat runs in Docker, there is one extra step. The Docker container cannot run the macOS Bear app binary, so Bear MCP has to run on the Mac host and LibreChat connects to it over HTTP.

That was the main gotcha in my setup.

Why use Bear’s official MCP server?

There are a few possible ways to connect an assistant to Bear notes. You could try a third-party integration, or even read Bear’s local database directly.

I would avoid that.

Bear already provides an official command-line tool called bearcli, and it can run an MCP server:

/Applications/Bear.app/Contents/MacOS/bearcli mcp-server

Using the official interface gives you a cleaner and safer path:

  • You do not need to reverse-engineer Bear’s database.
  • You avoid direct SQLite access to app data.
  • You use the tool Bear itself provides.
  • You can expose only the Bear-related tools to a dedicated agent.

This fits nicely with a local-first workflow.

The most important rule: read first, write later

Before configuring anything, it is worth deciding how the agent should behave.

For notes, I prefer a conservative workflow:

  1. Search before reading full notes.
  2. Prefer narrow queries instead of broad scans.
  3. Show note titles before opening many notes.
  4. Summarize first, modify later.
  5. Ask for explicit confirmation before every write operation.
  6. Ignore notes in Trash unless I explicitly ask for them.

This is important because an AI agent connected to your notes is not just a search box. Depending on the tools exposed, it may be able to create, edit, tag, archive, or trash notes.

That is useful, but only if the default behavior is safe.

Option 1: LibreChat running directly on macOS

If LibreChat is running directly on your Mac, you can configure Bear MCP as a stdio server.

Add this to librechat.yaml:

mcpServers:
  bear:
    type: stdio
    title: Bear Notes
    description: Search, read, create, append, tag, archive, and trash Bear notes through Bear's official bearcli MCP server.
    command: /Applications/Bear.app/Contents/MacOS/bearcli
    args:
      - mcp-server
    timeout: 30000
    initTimeout: 10000
    serverInstructions: |
      Use search before reading Bear notes.
      Exclude notes in Trash by default.
      For any operation that changes notes, show the exact proposed change first.
      Wait for explicit confirmation before writing, tagging, archiving, trashing, deleting, bulk-editing, or retagging.

This tells LibreChat to start Bear’s MCP server by running bearcli mcp-server.

If you see an error like this:

spawn /Applications/Bear.app/Contents/MacOS/bearcli ENOENT

it means LibreChat cannot find that executable. Either Bear is installed somewhere else, or LibreChat is not really running in the same macOS environment.

Which brings us to Docker.

Option 2: LibreChat running in Docker

If LibreChat runs inside Docker, the direct stdio setup will not work.

The LibreChat API container is a Linux environment. It cannot execute this macOS binary:

/Applications/Bear.app/Contents/MacOS/bearcli

So this configuration will fail inside Docker:

mcpServers:
  bear:
    type: stdio
    command: /Applications/Bear.app/Contents/MacOS/bearcli
    args:
      - mcp-server

Instead, run Bear MCP on the macOS host and expose it to LibreChat through a local MCP HTTP bridge.

The architecture becomes:

LibreChat Docker container
        ↓ HTTP
host.docker.internal:8080
        ↓ MCP HTTP bridge on macOS
Bear stdio MCP server
        ↓
/Applications/Bear.app/Contents/MacOS/bearcli mcp-server
        ↓
Bear notes

The exact bridge command depends on the MCP proxy you use. The key idea is always the same:

stdio MCP server → local HTTP MCP endpoint

Once your bridge exposes Bear MCP on your Mac, configure LibreChat to connect to it through host.docker.internal:

mcpSettings:
  allowedDomains:
    - host.docker.internal

mcpServers:
  bear:
    type: streamable-http
    title: Bear Notes
    description: Search and manage Bear notes through Bear's official MCP server.
    url: http://host.docker.internal:8080/mcp
    timeout: 30000
    initTimeout: 10000
    serverInstructions: |
      Use search before reading Bear notes.
      Exclude notes in Trash by default.
      If the tool returns notes from Trash, filter them out before using the results.
      For any operation that changes notes, show the exact proposed change first.
      Wait for explicit confirmation before writing, tagging, archiving, trashing, deleting, bulk-editing, or retagging.

Create the Bear Notes Agent

Now create a dedicated agent in LibreChat:

Endpoint selector → Agents → Agent Builder → Create Agent

Suggested setup:

Name: Bear Notes
Purpose: Browse, summarize, and carefully manage Bear notes
Tools: Bear MCP only
Default behavior: read-only
Writes: preview + explicit confirmation
Trash: excluded by default

I would not expose Bear tools casually in every chat. A separate agent is easier to reason about and safer to use.

Agent instructions

The agent instructions matter as much as the technical setup. You want the assistant to understand that Bear notes are not disposable scratch data.

Here is a practical instruction block:

You are an assistant for managing Bear notes.

Use the Bear MCP server only for Bear note operations. Search before reading full note contents. Prefer narrow, precise queries instead of broad scans.

Ignore notes in Trash by default. Do not include notes from Trash in search results, summaries, recommendations, cleanup plans, tagging suggestions, or any other response unless the user explicitly asks to inspect Trash. If the Bear tool returns notes from Trash, filter them out before using the results.

For read-only requests:
- List the Bear note titles you used.
- Do not include notes from Trash.
- Clearly separate note content from your own conclusions.
- Keep summaries concise unless the user asks for detail.
- If results are ambiguous, show candidate note titles first instead of reading many full notes.

For write or management operations:
- Never modify notes immediately.
- First show the planned operation.
- List the affected note titles or IDs.
- Confirm that none of the affected notes are in Trash.
- Show the exact text that will be created, appended, replaced, tagged, archived, moved to Trash, or otherwise changed.
- Wait for explicit user confirmation before using any modifying tool.

Never archive, trash, delete, bulk-edit, retag, merge, or reorganize notes unless the user explicitly asks for that specific operation in the current conversation.

You can make it shorter, but I would keep the confirmation rules. They are the difference between a useful notes assistant and a risky automation.

Example prompts

Once everything is connected, you can use prompts like:

Find my notes about LibreChat and summarize the setup steps. Do not include notes from Trash.

Search for notes about MCP servers and list the most relevant titles first.

Prepare a new summary note from the notes about Bear MCP, but show me the draft before creating it.

Find possible duplicate notes about AI credit balance plugins. Do not change anything yet.

The last part is important: do not change anything yet. For note management, preview-first is usually the right habit.

Troubleshooting

Docker cannot reach the MCP bridge

From the Docker container, the Mac host should usually be available as:

host.docker.internal

Make sure your MCP bridge is running on the host, listening on the expected port, and that the URL in LibreChat matches the bridge endpoint.

Privacy notes

This setup can stay local-first, but it still depends on the model you choose in LibreChat.

Bear MCP gives the agent access to your notes. LibreChat then sends the relevant note content to the selected AI model during the conversation.

So before using it with sensitive notes, think about:

  • which model provider you use,
  • whether the model runs locally or remotely,
  • what data you are comfortable sending,
  • and whether the agent really needs full note contents.

The safest pattern is still: search narrowly, read only what is needed, and modify only after confirmation.

Final thoughts

The main lesson is simple: do not run Bear MCP as stdio inside a LibreChat Docker container. Docker cannot execute Bear’s macOS binary.

Run Bear MCP on the Mac host, expose it through a local MCP HTTP bridge, and connect LibreChat to it through host.docker.internal.

Then wrap it in a dedicated Bear Notes Agent with conservative instructions.

That gives you the useful part of an AI notes assistant such as search, summaries, drafts, and careful organization.