How to Use the Determ MCP in AutoGen

Q: How do I load Determ tools into AutoGen?

Run pip install -U "autogen-extmcp" first. You then use mcpservertools with StreamableHttpServerParams to fetch the tools and pass them directly to your AssistantAgent.

Q: Does AutoGen handle the Determ API schemas automatically?

The framework uses McpToolAdapter to translate the MCP tool definitions into AutoGen's native format. Your agents instantly know what parameters searchmentionsbykeyword requires without manual configuration.

Build multi-agent systems that debate PR strategies and analyze media mentions using AutoGen and Determ.

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Connect Determ MCP to AutoGen

Create your Vinkius account to connect Determ 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 Determ with AutoGen

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Perplexity

PR crisis debate

AutoGen allows you to spin up competing agents to handle negative press coverage. A monitoring agent constantly polls `list_media_mentions` looking for new articles about your company. When it finds a spike in activity, it alerts a PR strategist agent to evaluate the situation. The strategist then runs `get_query_sentiment_summary` to see if the coverage is actually hostile. If the sentiment is overwhelmingly negative, a legal agent might step into the chat to debate the best public response. These agents negotiate a final statement based on consensus rather than a single LLM prompt.

Campaign analysis negotiation

Evaluating a marketing campaign requires looking at the data from multiple angles. You can assign one agent to pull performance metrics using `list_analytics_reports`. A separate data-critic agent reviews those numbers and challenges any optimistic assumptions. To settle the argument, the first agent might call `get_monitoring_query_details` to verify the exact tracking parameters used. They discuss whether the keyword setup was flawed or if the campaign actually succeeded. This back-and-forth deliberation ensures your final marketing report is thoroughly vetted before a human ever reads it.

Media outreach strategy via MCP Server

Planning your next press release works best when agents argue about targeting. An outreach agent executes `list_recent_high_reach_mentions` through the MCP Server to find out who covered your last announcement. It drafts a pitch list based purely on audience size. A brand-safety agent then steps in and calls `list_top_media_sources` to check the reputation of those publishers. If a high-reach site has a history of publishing hit pieces, the safety agent forces a revision of the pitch list. The `McpToolAdapter` handles all the schema conversions so your agents can focus on the debate.

Setup guide

Set up Determ 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 Determ 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="Determ_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Run `pip install -U "autogen-ext[mcp]"` first. You then use `mcp_server_tools` with `StreamableHttpServerParams` to fetch the tools and pass them directly to your `AssistantAgent`.

Yes, you can provide the same tool list to different agents in your conversation. A researcher agent can pull mentions while a writer agent drafts responses based on that data.

The framework uses `McpToolAdapter` to translate the MCP tool definitions into AutoGen's native format. Your agents instantly know what parameters `search_mentions_by_keyword` requires without manual configuration.

If an agent misinterprets a sentiment summary, another agent in the chat can challenge that conclusion. They will debate the findings and potentially call `get_mention_details` to verify the raw text before agreeing.

Your media mentions and account metadata are processed entirely in memory. Vinkius destroys the sandbox immediately after the API call finishes, keeping your PR strategy completely private.

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