How to Use the Bilibili Live MCP in AutoGen

Q: How do I register Bilibili Live tools with AutoGen agents?

Install autogen-extmcp and use mcpservertools with your server URL. Pass the resulting tool list directly to your AssistantAgent to let them run stream operations.

Q: How do AutoGen agents handle schema conversion for these tools?

The framework uses McpToolAdapter to automatically convert the Bilibili Live JSON schemas into the format expected by the underlying LLMs, requiring zero manual mapping.

Q: How does the framework protect my Bilibili Live chat logs?

All chat data pulled via getsuperchats is processed within isolated runtime environments. Vinkius enforces strict data-in-transit encryption, meaning your private viewer interactions are never leaked to third-party logging tools.

Deploy AutoGen multi-agent teams to debate stream moderation, verify fan ranks, and coordinate live Bilibili Live actions.

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Connect Bilibili Live MCP to AutoGen

Create your Vinkius account to connect Bilibili Live to AutoGen 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 Bilibili Live with AutoGen

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Run consensus-driven Bilibili Live room updates

This MCP Server allows multiple AutoGen agents to collaborate on Bilibili stream management. An AutoGen moderation agent checks current streams using `get_room_play_info`, while an AutoGen copywriting agent drafts potential title updates.

Coordinate AutoGen agents for Bilibili Live fan rewards

This MCP Server provides the raw Bilibili data needed for multi-agent AutoGen viewer analysis. One AutoGen agent pulls active VIPs using `get_guard_list`, while a second AutoGen agent fetches their loyalty levels via `get_fans_medal_info`.

Multi-agent chat moderation with AutoGen and MCP

This MCP Server enables real-time collaborative Bilibili chat monitoring within AutoGen. An AutoGen safety agent scans active paid messages using `get_super_chats` and flags potential Bilibili spam or policy violations.

Setup guide

Set up Bilibili Live MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Bilibili Live tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Bilibili Live_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Bilibili Live data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Bilibili Live_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Bilibili Live data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bilibili Live. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Bilibili Live now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Bilibili Live MCP in AutoGen

Install `autogen-ext[mcp]` and use `mcp_server_tools` with your server URL. Pass the resulting tool list directly to your `AssistantAgent` to let them run stream operations.

Yes, you can set up one agent to fetch incoming items using `get_gift_history` and another to calculate total stream revenue. They will discuss the data before deciding on a live response.

Yes, your agents can pull the required socket details using `get_danmu_config` and pass them to a dedicated connection agent that handles the live data stream.

The framework uses `McpToolAdapter` to automatically convert the Bilibili Live JSON schemas into the format expected by the underlying LLMs, requiring zero manual mapping.

All chat data pulled via `get_super_chats` is processed within isolated runtime environments. Vinkius enforces strict data-in-transit encryption, meaning your private viewer interactions are never leaked to third-party logging tools.

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