How to Use the Ghost MCP in AutoGen

Q: How do I configure AutoGen with the Ghost MCP?

Install autogen-extmcp. Use mcpservertools with a StreamableHttpServerParams pointing to your endpoint, then pass the resulting list to your AssistantAgent constructor.

Deploy AutoGen multi-agent teams to debate and analyze your Ghost CMS content strategy via this MCP Server.

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Create your Vinkius account to connect Ghost 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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Perplexity

Editorial Debate with AutoGen

The `list_published_posts` tool feeds raw publication data directly into your AutoGen multi-agent chats. An SEO agent grabs the post list, while a content strategist agent runs `get_post_by_slug` to read the copy. They debate the results. The SEO agent flags missing keywords, while the strategist argues for readability. They negotiate a final content audit report based on real API data, not assumptions.

Taxonomy & Author Verification

Your AutoGen agents use `list_content_tags` to challenge each other on site architecture. A structural agent pulls the tags, then uses `get_tag_details` to prove to the team that certain categories lack recent posts. The debate extends to team management. An agent pulls `list_blog_authors` and `get_author_details` to argue for reassigning topics based on past publication volume. They reach a consensus before outputting the final recommendation.

Monetization Strategy Review

The `list_subscription_tiers` tool allows a financial agent to audit your Ghost paywall setup through the MCP Server. It compares the pricing tiers against the copy pulled via `list_static_pages` and `get_page_by_slug`. If the static page promises a feature that the subscription tier API says is missing, the agents flag the discrepancy. They run `get_site_settings` to verify the global configuration and `verify_api_connection` to ensure they have the latest data before concluding the chat.

Setup guide

Set up Ghost 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 Ghost 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="Ghost_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with a `StreamableHttpServerParams` pointing to your endpoint, then pass the resulting list to your AssistantAgent constructor.

Yes. The MCP adapter converts the schema so any agent in the chat can call the endpoints. They share the retrieved data in the conversation history.

No. The integration is strictly for reading data. They debate the content and output a consensus report, but you must manually apply their recommendations in the CMS.

You configure the agents with strict system prompts regarding tool usage frequency. Since they discuss actions before executing, they naturally throttle their calls to the MCP Server.

The agents read author names, post slugs, and subscription pricing. They pull full article bodies into their conversation context, meaning your LLM provider processes that specific text.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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