How to Use the CaptionHub MCP in AutoGen

Debate your video localization strategy in AutoGen using the CaptionHub MCP Server.

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Connect CaptionHub MCP to AutoGen

Create your Vinkius account to connect CaptionHub 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 CaptionHub with AutoGen

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Perplexity

Debate CaptionHub tasks with AutoGen

Assign one agent to `transcribe_video` and another to review the results. They discuss whether the transcription meets your quality bar before moving to the next stage. This forces a consensus on complex projects. You aren't just running a script; you're building a system that challenges its own assumptions before touching live data.

Negotiate project changes between agents

Let a security agent and a project manager agent debate `update_project` calls. One checks for permission risks while the other pushes for speed, converging on a safe, approved action. Your workflow benefits from this deliberation. The agents reach a decision that balances your requirements without human intervention.

Automate project archival with consensus

Use `archive_project` only after agents agree the project is complete. They check the status via `get_project_details` and ensure no pending captions exist before firing the command. This prevents accidental data loss. The agents verify the state, confirm the intent, and only then perform the permanent archival.

Setup guide

Set up CaptionHub 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 CaptionHub 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="CaptionHub_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent CaptionHub 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="CaptionHub_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent CaptionHub 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 CaptionHub. 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 CaptionHub MCP in AutoGen

The tool adapter converts the server schema so every agent in your group chat can see the available functions. You pass the tool list to your agents, and they decide who calls what based on their assigned roles.

Agents will naturally debate the results returned by tools like `get_project_details`. If the returned metadata is ambiguous, they will discuss how to proceed before taking further action.

Yes, the adapter handles the schema conversion for both stdio and HTTP transports. Your agents can call any of the ten available tools as part of their collaborative reasoning process.

You provide the endpoint token during the server parameter setup. From there, your agents use the tools as needed to complete the tasks you define in your chat initiation.

The server acts as a secure gateway for your account information and project data. Your agents only see the text-based responses returned by the API, and all traffic is encrypted with your provided token.

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