How to Use the Monnify MCP in LlamaIndex

Q: How do I connect the Monnify MCP Server to my LlamaIndex RAG application?

Install the llama-index-tools-mcp package and instantiate the BasicMCPClient pointing to your Vinkius server URL. Convert the server's tools into a tool list and pass them to your LlamaIndex FunctionAgent.

Q: Can LlamaIndex index reserved virtual accounts from Monnify?

Yes, the agent can call listreservedaccounts and index the results. This allows you to perform semantic searches over active virtual accounts to find specific customer allocations.

Q: How does LlamaIndex handle transaction query errors with Monnify?

If gettransaction fails or returns a pending status, the LlamaIndex agent logs the response within its memory buffer. The agent then decides whether to retry the query or update its index with the pending state.

Q: Can I initialize payments using this MCP Server in LlamaIndex?

Yes, the initpayment tool is fully exposed to your LlamaIndex agent. It can generate payment links based on user queries and immediately add the transaction metadata to its vector index.

Index Nigerian payment data and automate virtual account management using LlamaIndex and this MCP Server.

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Connect Monnify MCP to LlamaIndex

Create your Vinkius account to connect Monnify to LlamaIndex 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 Monnify with LlamaIndex

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Build a queryable ledger with LlamaIndex

`list_transactions` fetches recent Monnify payment records so your LlamaIndex pipeline can index them into your vector store. Your LlamaIndex agent queries this indexed Monnify data to answer complex financial questions without executing fresh API calls every time. This LlamaIndex approach eliminates repetitive database queries for Monnify records. By indexing the Monnify transaction history, your LlamaIndex agent instantly matches past payments with customer support tickets to resolve disputes in seconds.

Semantic search over digital invoices

`list_monnify_invoices` pulls active Monnify billing records directly into your LlamaIndex document store via this MCP Server. The LlamaIndex agent parses these Monnify invoices, making them searchable by customer name, amount, or payment status. You get a unified LlamaIndex interface where your agent retrieves Monnify invoice details dynamically. If a Monnify invoice is overdue, the LlamaIndex agent uses the indexed data to draft a personalized reminder containing the specific payment link.

Real-time settlement audit trails

`list_settlements` tracks Monnify merchant payouts and merges them with your local LlamaIndex financial index. This Monnify tool feeds settlement arrays to your LlamaIndex agent, which cross-references them against internal ledger documents. Discrepancies disappear when your LlamaIndex agent can look up Monnify settlement IDs and match them to individual transactions. The LlamaIndex agent highlights any un-reconciled Monnify payouts so your finance team can review them.

Setup guide

Set up Monnify MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Monnify MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Monnify tools.",
)
response = await agent.run("List recent Monnify data")

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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 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 LlamaIndex

Install the `llama-index-tools-mcp` package and instantiate the `BasicMCPClient` pointing to your Vinkius server URL. Convert the server's tools into a tool list and pass them to your LlamaIndex `FunctionAgent`.

Yes, the agent can call `list_reserved_accounts` and index the results. This allows you to perform semantic searches over active virtual accounts to find specific customer allocations.

If `get_transaction` fails or returns a pending status, the LlamaIndex agent logs the response within its memory buffer. The agent then decides whether to retry the query or update its index with the pending state.

Yes, the `init_payment` tool is fully exposed to your LlamaIndex agent. It can generate payment links based on user queries and immediately add the transaction metadata to its vector index.

Vinkius executes the server in an ephemeral, zero-trust sandbox. Your settlement records and invoice details are never persisted, keeping your merchant financial reports secure and compliant.

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