How to Use the Modulr MCP in AutoGen
Deploy consensus-driven AutoGen agent teams to coordinate, review, and execute high-volume Modulr payments safely.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Modulr MCP to AutoGen
Create your Vinkius account to connect Modulr 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.
Consensus-Driven Account Creation
The `modulr_create_account` tool allows your AutoGen developer agent to spin up new GBP or EUR ledgers after gaining approval from your compliance agent. In this multi-agent setup, one agent requests the ledger while another verifies that the customer profile is fully onboarding-compliant. This collaborative check prevents unauthorized ledger creation. Once both agents agree that the parameters meet your compliance rules, the designated execution agent triggers the tool to establish the live account.
Multi-Agent Beneficiary Verification
Using the `modulr_create_beneficiary` tool, your AutoGen setup maps external recipients and IBANs only after your security agent completes an automated risk assessment over the MCP transport layer. Your security agent audits the destination routing details, while the operations agent confirms the transfer limits. By separating these duties, you eliminate single-point-of-failure risks in automated payouts. The tool is only called when the conversation reaches a verified consensus, protecting your company from fraudulent routing changes.
Debated Payment Execution via AutoGen MCP Server
The `modulr_create_payment` tool processes outgoing SEPA or Faster Payments after your agent team debates and approves the transaction details. A performance agent pushes for immediate execution while a risk agent validates the transaction history using `modulr_get_transactions`. This debate ensures that no massive payment array is processed blindly. Once they reach a consensus, the execution agent triggers the payout and reports the final settlement status to the group.
Set up Modulr 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Modulr tools and returns structured results.
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="Modulr_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Modulr data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Modulr_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Modulr data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Modulr Finance. 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 Modulr MCP in AutoGen
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