Mollie MCP Server for LlamaIndexGive LlamaIndex instant access to 7 tools to Create Payment, Get Payment Details, List Customer Subscriptions, and more
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.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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())
* 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 payment on Mollie
Create a new payment
Get payment details on Mollie
Get details for a specific payment
List customer subscriptions on Mollie
List subscriptions for a customer
List customers on Mollie
List Mollie customers
List payment methods on Mollie
List available payment methods
List payments on Mollie
List Mollie payments
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.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Mollie MCP Server
LlamaIndex provides unique advantages when paired with Mollie through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mollie tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mollie tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mollie, a vector store, and a SQL database in a single turn and synthesize results
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.
Hybrid search: combine Mollie real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mollie to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Mollie for fresh data
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.
"List recent payments and their statuses."
"Create a payment of €50 for a premium upgrade."
"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.
BasicMCPClient not found
pip install llama-index-tools-mcpMollie + LlamaIndex FAQ
Common questions about integrating Mollie MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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