How to Use the Cosmic (Headless CMS & Content API) MCP in AutoGen

Q: Can AutoGen agents discuss Cosmic (Headless CMS & Content API) revisions?

Yes, by exposing listrevisions to your agents, they can debate the history of a specific object and decide on the best version to keep.

Q: How do I configure Cosmic (Headless CMS & Content API) with AutoGen?

Use the mcpservertools function with your HTTP URL to generate the schema. Pass the resulting list into your AssistantAgent constructor.

Debate and refine your CMS content strategy by deploying the Cosmic (Headless CMS & Content API) MCP Server with AutoGen.

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Setup Cosmic (Headless CMS & Content API) with AutoGen

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Multi-agent content moderation in AutoGen

Set up a debate between a compliance agent and a content editor agent using `get_object` and `update_object`. The agents challenge each other on whether a draft meets your standards. This ensures every piece of content undergoes rigorous review before it hits your bucket. Your team saves hours by letting agents negotiate the final state of your records.

Negotiate CMS schema changes

Let your AutoGen agents argue the merits of a new `create_object_type` request. One agent focuses on data normalization while the other prioritizes developer speed. They reach a consensus on the final schema, which you then execute via the MCP server. This keeps your CMS structure clean and consistent without manual oversight.

Resolve content conflicts through debate

Use the conversation history tools like `get_agent_conversation_history` to let agents review their previous arguments. They iterate on their logic until they reach an agreement on how to resolve a record conflict. This framework allows your AutoGen agents to learn from past interactions. They build a smarter, more resilient content management process through constant deliberation.

Setup guide

Set up Cosmic (Headless CMS & Content API) 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 Cosmic (Headless CMS & Content API) 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="Cosmic (Headless CMS & Content API)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cosmic (Headless CMS & Content API) 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="Cosmic (Headless CMS & Content API)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Cosmic (Headless CMS & Content API) 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 Cosmic. 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 Cosmic (Headless CMS & Content API) MCP in AutoGen

Yes, by exposing `list_revisions` to your agents, they can debate the history of a specific object and decide on the best version to keep.

Use the `mcp_server_tools` function with your HTTP URL to generate the schema. Pass the resulting list into your AssistantAgent constructor.

They share the state through the conversation context. Any updates made by one agent are immediately visible to the others.

The data is safely persisted in your Cosmic bucket. The ephemeral server environment is destroyed immediately after your task completes.

The server uses isolated memory for every single interaction. Your raw content objects are processed in a secure sandbox and never logged.

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