How to Use the Modulr MCP in LlamaIndex

Q: How do I connect the Modulr MCP server to LlamaIndex?

Install the adapter with pip install llama-index-tools-mcp. Initialize the BasicMCPClient with your Vinkius URL, wrap it in a McpToolSpec, and call totoollistasync() to expose the payment tools to your FunctionAgent.

Q: Can LlamaIndex query historical Modulr payments?

Yes. Your agent can use modulrlistpayments to pull historical records, which are then parsed and loaded into your local document index for semantic search and audit reporting.

Q: How does LlamaIndex prevent hallucinated Modulr transactions?

By grounding the agent's context in raw data from modulrgetaccounts. The agent is forced to use the actual numeric balances and account IDs returned by the API rather than guessing from past conversational history.

Index live European transaction data and automate secure payouts using LlamaIndex knowledge-augmented agents.

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Connect Modulr MCP to LlamaIndex

Create your Vinkius account to connect Modulr 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 Modulr with LlamaIndex

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Index Transaction Data in LlamaIndex

The `modulr_get_transactions` tool fetches complete ledger histories directly into your index pipelines using this MCP server. Instead of querying static databases, your LlamaIndex application pulls live banking data to keep your vector index completely synchronized with actual bank movements. This live data feeds your retrieval-augmented generation (RAG) loops. When users ask about recent account activity, the agent queries the vector store containing these freshly indexed transaction logs to provide answers grounded in real financial facts.

Dynamic Customer Mapping with LlamaIndex

The `modulr_get_customers` tool retrieves underlying legal entities and customer profiles managed within your account structure. Your agent indexes these profiles to build a searchable knowledge graph of all registered merchants and their verification states. When setting up new payout targets, the agent cross-references this indexed customer data. Doing this ensures that any subsequent call to `modulr_create_beneficiary` uses verified, compliant legal names and prevents routing errors before they happen.

Execute Verified Payouts via LlamaIndex MCP Server

The `modulr_create_payment` tool executes SEPA and Faster Payments using real-time context retrieved from your knowledge base. Before triggering the payment, the agent checks the indexed balance sheets to confirm that the source ledger has sufficient liquidity. After execution, the agent automatically logs the payment ID and status. It immediately updates the vector store, ensuring that the very next query reflects the reduced balance without waiting for end-of-day batch processing.

Setup guide

Set up Modulr 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 Modulr 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 Modulr tools.",
)
response = await agent.run("List recent Modulr 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 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 LlamaIndex

Install the adapter with `pip install llama-index-tools-mcp`. Initialize the `BasicMCPClient` with your Vinkius URL, wrap it in a `McpToolSpec`, and call `to_tool_list_async()` to expose the payment tools to your `FunctionAgent`.

Yes. Your agent can use `modulr_list_payments` to pull historical records, which are then parsed and loaded into your local document index for semantic search and audit reporting.

By grounding the agent's context in raw data from `modulr_get_accounts`. The agent is forced to use the actual numeric balances and account IDs returned by the API rather than guessing from past conversational history.

Yes. When setting up the MCP tool specification, you can use the `allowed_tools` filter to restrict your agent's access, ensuring it can only view accounts and not trigger payments if you want a read-only setup.

Vinkius routes all customer profiles and IBAN data through zero-trust, ephemeral MCP connections. Credentials are never written to persistent disks, and all API communication is encrypted in transit using strict TLS protocols.

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