How to Use the Monnify MCP in AutoGen
Deploy debating agents to manage Nigerian virtual accounts and process payments using AutoGen and this MCP Server.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Monnify MCP to AutoGen
Create your Vinkius account to connect Monnify 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.
Debate-driven virtual account reservation via MCP Server
`reserve_account` generates Monnify virtual bank accounts after your AutoGen agents debate and approve the customer's risk profile. A compliance AutoGen agent checks user data while a support AutoGen agent requests the Monnify account, ensuring no accounts are created without validation. This collaborative AutoGen process stops fraudulent Monnify registrations before they hit the API. Once the AutoGen agents reach a consensus, the primary execution agent triggers the Monnify tool and shares the account details across the group.
Consensus-based disbursement validation
`list_disbursements` retrieves outgoing Monnify transfer records so your AutoGen agents can cross-verify them. A finance AutoGen agent reviews the Monnify transfer history, while an audit AutoGen agent checks the list against internal ledgers to confirm payout accuracy. Your AutoGen system gains a layer of protection against unauthorized Monnify transfers. If the AutoGen agents find a mismatch in the Monnify disbursement data, they halt further operations until a human operator resolves the dispute.
Multi-agent payment verification
`get_transaction` pulls specific Monnify transaction statuses to help your AutoGen agents resolve payment disputes. One AutoGen agent fetches the Monnify transaction data, another verifies the customer's claim, and a third decides whether to trigger a refund. This division of labor ensures that no single AutoGen agent has unilateral control over your Monnify funds. By dividing the Monnify verification and action steps among AutoGen agents, your system maintains strict operational security.
Set up Monnify 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 Monnify 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="Monnify_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Monnify 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="Monnify_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Monnify 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 Monnify. 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 Monnify MCP in AutoGen
Use it with your favorite AI tools
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