How to Use the Bear MCP in OpenAI Agents SDK

Q: Can the OpenAI Agents SDK search my Bear tags?

Yes. The agent uses listtags to retrieve your entire nested tag taxonomy. From there, it can filter notes or rename tags globally across your database.

Run OpenAI Agents SDK pipelines that directly search, write, and organize your Bear notes with built-in validation.

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Connect Bear MCP to OpenAI Agents SDK

Create your Vinkius account to connect Bear to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Bear with OpenAI Agents SDK

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Automated Note Construction with OpenAI Agents SDK

This MCP Server exposes the `create_note` and `add_text` tools so your agent can spin up new markdown files and append raw text on the fly. Your agent reads your instructions, checks for existing files, and writes structured updates directly into your local database without manual copy-pasting. Because the SDK handles agent handoffs, you can have a researcher agent gather data and then pass the payload to a writer agent. The writer agent calls these tools to format the markdown and structure your personal wiki automatically.

Targeted Search and Tag Restructuring

The `search_notes` and `open_tag` tools let your agent scan your entire markdown library to find specific research papers or journals. It doesn't guess; it queries the actual Bear database to pull precise note contents. If you need to clean up your taxonomy, the agent can run `rename_tag` or `delete_tag` across hundreds of files in one shot. This replaces manual tagging chores with a single conversational command to your agent.

Safe Note Archiving and Trash Management

Using the `archive_note` and `trash_note` tools, your agent can safely clean up outdated drafts and archive completed projects. You don't have to hunt down old files to keep your workspace clean. The SDK's built-in guardrails validate every destructive action before running the MCP tool, ensuring the agent never trashes an active note by mistake. You get a clean workspace with zero risk of losing critical data.

Setup guide

Set up Bear MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Bear tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Bear tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Bear tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Bear Agent",
            instructions="You have access to Bear tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bear. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Bear MCP in OpenAI Agents SDK

Install the SDK and point it to the Vinkius managed endpoint. Use the `MCPServerStreamableHttp` class inside an async context manager and pass it to your Agent constructor. The tools are auto-discovered immediately.

Yes. The agent uses `list_tags` to retrieve your entire nested tag taxonomy. From there, it can filter notes or rename tags globally across your database.

The SDK's guardrails intercept the call. When the agent triggers `trash_note`, the SDK validates the action against your safety rules before executing it.

No. Vinkius hosts the MCP Server in a secure sandbox. Your Python code connects to our high-performance endpoint, so you don't have to manage any local background processes.

Your markdown text and tag taxonomy never persist on Vinkius servers. Every request runs in an isolated, ephemeral V8 sandbox that destroys all session data the moment the agent finishes its call.

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