How to Use the FirstQuadrant MCP in AutoGen

Q: Can AutoGen agents collaborate on FirstQuadrant enrichment?

Yes. One agent can identify leads using listcontacts, while another calls enrichcontactai and checks the results, ensuring multiple eyes verify the lead quality.

Q: How do AutoGen agents handle campaign errors in FirstQuadrant?

If addtocampaign fails, the executing agent shares the error with the group. A developer agent can then analyze the issue and suggest a fix based on listpropertiesschema.

Build multi-agent sales teams in AutoGen that debate lead quality and coordinate outreach campaigns.

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Connect FirstQuadrant MCP to AutoGen

Create your Vinkius account to connect FirstQuadrant 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 FirstQuadrant with AutoGen

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Coordinate AutoGen campaigns with multi-agent debate

The `list_campaigns` tool lets your agents coordinate AutoGen campaigns with multi-agent debate. One agent can pull active sequences while a separate qualifying agent evaluates the lead data from `get_contact_details`. They debate the fit before calling `add_to_campaign`. This consensus-driven approach stops poor-fit leads from entering your high-value sales sequences and ruining your domain reputation.

Run autonomous multi-agent lead enrichment

The `enrich_contact_ai` tool powers autonomous multi-agent lead enrichment within your research loop. A researcher agent calls `create_contact` to log a new lead, then a second agent triggers the enrichment tool to gather missing professional data. They verify the results against `list_properties_schema` to make sure the custom fields match your CRM requirements. No single agent has to handle the entire workflow alone.

Audit sales activities via agent consensus

The `list_activities` tool lets your agents audit sales activities via group consensus. A supervisor agent can call this tool to review what outbound tasks were executed, comparing them against the target list from `list_contacts`. If an error is found, the agents collaborate to fix the contact record. This self-healing process ensures your sales pipeline remains clean and free of duplicate tasks.

Setup guide

Set up FirstQuadrant 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 FirstQuadrant 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="FirstQuadrant_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Use the MCP tool adapter to convert the server schema. Pass the tools directly into your AssistantAgent constructor so they can invoke commands like `create_contact` during conversations.

Yes. One agent can identify leads using `list_contacts`, while another calls `enrich_contact_ai` and checks the results, ensuring multiple eyes verify the lead quality.

If `add_to_campaign` fails, the executing agent shares the error with the group. A developer agent can then analyze the issue and suggest a fix based on `list_properties_schema`.

Yes. The Vinkius setup works with both transport layers, letting you connect your multi-agent conversations through the method that fits your deployment.

The MCP Server processes all sales activities and campaign data within an isolated, zero-trust sandbox. No contact details are stored or logged on our end, keeping your sales funnel secure.

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