How to Use the Mattermark MCP in AutoGen

Enable multi-agent debate and decision-making using Mattermark data in AutoGen.

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Connect Mattermark MCP to AutoGen

Create your Vinkius account to connect Mattermark 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 Mattermark with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-based market analysis

Let your agents argue over the metrics from `get_company_funding_rounds`. One agent acts as the skeptic while another presents the growth numbers. Force a debate before reaching a conclusion. This prevents your agents from jumping to premature decisions based on incomplete startup data.

Autonomous data negotiation

Assign agents to different roles like 'Researcher' and 'Analyst'. The Researcher fetches data via `list_investors` while the Analyst evaluates the signal. Watch them negotiate the findings. They'll ping-pong data back and forth until the consensus meets your strict criteria for a high-quality lead.

Dynamic tool delegation

Allow your AutoGen agents to choose which tool to run. If the first search fails, the agent might decide to try `search_companies` with a broader scope. Give your agents the autonomy to correct their own research paths. They handle the heavy lifting of iterating through the API until they get the right answer.

Setup guide

Set up Mattermark 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 Mattermark 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="Mattermark_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

They pass the tool outputs through the conversation context. Every agent in the group chat can see the results of previous tool calls and build upon them.

They can. You define the logic for how they resolve disagreements. One agent can challenge the accuracy of a funding round result, forcing another to re-verify.

Your keys are injected as environment variables. The MCP server handles the handshake, ensuring your credentials never leak into the agent's conversation logs.

The data is treated as a shared message history. Your agents process the raw JSON from the server and debate the interpretation within the chat thread.

The data remains within your local agent execution environment. It is never transmitted to third parties, ensuring your proprietary investment research stays strictly confidential.

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