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Mollie MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Create Payment, Get Payment Details, List Customer Subscriptions, and more

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LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mollie as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Ask AI about this MCP Server for LlamaIndex

The Mollie MCP Server for LlamaIndex is a standout in the Money Moves category — giving your AI agent 7 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Mollie. "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Mollie?"
    )
    print(response)

asyncio.run(main())
Mollie
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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Mollie MCP Server

Connect your Mollie account to any AI agent and manage European payments through natural conversation.

LlamaIndex agents combine Mollie tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

  • Payment Management — Create, list, and inspect payments with full status details
  • Refund Tracking — Browse all processed refunds
  • Customer Management — List registered customers for recurring billing
  • Subscriptions — View active and cancelled subscriptions per customer
  • Payment Methods — List all enabled payment methods (iDEAL, Bancontact, cards, etc.)

The Mollie MCP Server exposes 7 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 7 Mollie tools available for LlamaIndex

When LlamaIndex connects to Mollie through Vinkius, your AI agent gets direct access to every tool listed below — spanning payment-gateway, recurring-billing, refund-management, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create payment on Mollie

Create a new payment

get

Get payment details on Mollie

Get details for a specific payment

list

List customer subscriptions on Mollie

List subscriptions for a customer

list

List customers on Mollie

List Mollie customers

list

List payment methods on Mollie

List available payment methods

list

List payments on Mollie

List Mollie payments

list

List refunds on Mollie

List all refunds

Connect Mollie to LlamaIndex via MCP

Follow these steps to wire Mollie into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 7 tools from Mollie

Why Use LlamaIndex with the Mollie MCP Server

LlamaIndex provides unique advantages when paired with Mollie through the Model Context Protocol.

01

Data-first architecture: LlamaIndex agents combine Mollie tool responses with indexed documents for comprehensive, grounded answers

02

Query pipeline framework lets you chain Mollie tool calls with transformations, filters, and re-rankers in a typed pipeline

03

Multi-source reasoning: agents can query Mollie, a vector store, and a SQL database in a single turn and synthesize results

04

Observability integrations show exactly what Mollie tools were called, what data was returned, and how it influenced the final answer

Mollie + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Mollie MCP Server delivers measurable value.

01

Hybrid search: combine Mollie real-time data with embedded document indexes for answers that are both current and comprehensive

02

Data enrichment: query Mollie to augment indexed data with live information before generating user-facing responses

03

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Mollie for fresh data

04

Analytical workflows: chain Mollie queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Mollie in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Mollie immediately.

01

"List recent payments and their statuses."

02

"Create a payment of €50 for a premium upgrade."

03

"Show enabled payment methods and list refunds."

Troubleshooting Mollie MCP Server with LlamaIndex

Common issues when connecting Mollie to LlamaIndex through Vinkius, and how to resolve them.

01

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Mollie + LlamaIndex FAQ

Common questions about integrating Mollie MCP Server with LlamaIndex.

01

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
02

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Mollie tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
03

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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