How to Use the Spiritme MCP in AutoGen

Q: Does Spiritme provide data on deleted videos when using AutoGen?

Yes. The deletevideo tool allows you to manage content lifecycles. You can build an agent that tracks successful deletions and logs the reasons for removal.

Run consensus-driven campaigns with AutoGen using Spiritme.

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Connect Spiritme MCP to AutoGen

Create your Vinkius account to connect Spiritme to AutoGen — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Spiritme with AutoGen

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Negotiate Video Specs via MCP Server

The `generate_video` tool is central to the debate. Different agents can argue over the optimal video parameters: one might push for a high-res avatar (`get_avatar`), while another insists on a specific template ID (`list_templates`). The system converges on the final, executable call. This consensus model means you don't just run a command; you force your agents to *agree* on the best way to build and render the video.

Review Available Assets with AutoGen

Agents can use `list_avatars`, `list_voices`, and `list_templates` to gather all options for deliberation. For example, a 'Compliance Agent' might check if the required avatar is available in the target region before the 'Marketing Agent' even proposes it. This ensures that the final decision isn't just based on what's easiest, but what's actually feasible across all defined constraints.

Verify and Delete Content with AutoGen

Before committing to a costly generation job, agents can use `get_job_status` to check if the video is already running or completed. If it's stale content that needs removal, the `delete_video` tool allows one agent (the 'Cleanup Agent') to confidently retire the asset. This structure builds accountability into your system; every action must be justified by a successful debate.

Setup guide

Set up Spiritme 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 Spiritme 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="Spiritme_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="Spiritme_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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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 Spiritme. 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 Spiritme MCP in AutoGen

AutoGen doesn't just execute tasks; it simulates discussion. Multiple agents can challenge each other's inputs (like which voice to use), forcing a consensus on the final, correct video configuration.

The tools support avatars, templates, and voices. Agents can debate which combination works best for the message—for instance, determining if a complex script needs an audio generation step or just a simple voice recording.

It's a foundational tool. Before any agent starts debating, they can use this to verify that the entire MCP Server connection is live and healthy, preventing wasted deliberation time.

Yes. The `delete_video` tool allows you to manage content lifecycles. You can build an agent that tracks successful deletions and logs the reasons for removal.

This includes video metadata, template structures, avatar IDs, voice details, and job status. The system handles all these parameters during its multi-agent deliberation process.

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