---
title: "Connect Mollie to AI Agents: Manage Refunds, Invoices, and Settlements"
slug: connect-mollie-to-ai-agents-manage-refunds-invoices-and-settlements
date: 2026-07-16
author: Sidharth Verma
categories: ["AI & Agents"]
excerpt: "Learn how to connect Mollie to AI agents using Truto's /tools endpoint. Build autonomous FinOps workflows to manage refunds, settlements, and invoices."
tldr: "A comprehensive engineering guide on connecting Mollie to AI agents. We cover handling Mollie's API quirks, fetching LLM-ready tools, and architecting multi-step workflows for autonomous FinOps."
canonical: https://truto.one/blog/connect-mollie-to-ai-agents-manage-refunds-invoices-and-settlements/
---

# Connect Mollie to AI Agents: Manage Refunds, Invoices, and Settlements


You want to connect Mollie to an AI agent so your financial systems can independently process refunds, reconcile settlements, query open balances, and generate sales invoices. Here is exactly how to do it using Truto's `/tools` endpoint and SDK, bypassing the need to hand-code complex API wrappers for your FinOps workflows.

Giving a Large Language Model (LLM) read and write access to your Mollie payment infrastructure requires extreme precision. You either spend weeks building, hosting, and maintaining a custom connector that handles complex pagination and strict payload schemas, or you use a managed infrastructure layer that provides normalized, AI-ready tool definitions. If your team uses ChatGPT for daily financial operations, check out our guide on [connecting Mollie to ChatGPT](https://truto.one/connect-mollie-to-chatgpt-streamline-payments-and-revenue-tracking/). If you are building automated FinOps routines on Anthropic's models, read our guide on [connecting Mollie to Claude](https://truto.one/connect-mollie-to-claude-automate-subscriptions-and-payouts/). For developers building custom autonomous workflows, you need a programmatic way to fetch these tools and bind them to your agent framework.

This guide breaks down exactly how to fetch AI-ready tools for Mollie, bind them natively to an LLM using LangChain (or any framework like LangGraph, CrewAI, or Vercel AI SDK), and execute complex revenue operations workflows safely. For a deeper look at the architecture behind this approach, refer to our research on [architecting AI agents and the SaaS integration bottleneck](https://truto.one/architecting-ai-agents-langgraph-langchain-and-the-saas-integration-bottleneck/).

## The Engineering Reality of Custom Mollie Connectors

Building AI agents is straightforward. Connecting them to highly regulated external financial APIs is difficult. Giving an LLM raw access to external data sounds simple during prototyping - you write a Node.js function that makes a fetch request and wrap it in an `@tool` decorator. In production, this approach collapses entirely, especially with an ecosystem as rigid as Mollie.

If you decide to build the integration yourself, you own the entire API lifecycle. Mollie's REST API is impeccably designed, but it introduces several highly specific integration challenges that break standard LLM assumptions.

### The HAL Pagination Problem
Mollie uses HAL (Hypertext Application Language) for pagination. Instead of passing simple `page=2` or `offset=50` parameters, the API returns a `_links` object containing `next`, `previous`, and `last` href URLs. 

When an agent needs to retrieve 300 transactions to reconcile a settlement, standard REST cursor conventions fail. You cannot expect an LLM to parse a nested `_links.next.href` string, extract the cursor parameter, and formulate the subsequent request accurately. When you hand-code this integration, you are forced to write complex wrappers just to abstract HAL pagination away from the model. If you do not, the LLM will hallucinate cursor strings and crash your integration loops.

### Asynchronous State Machines
Financial operations in Mollie - like refunds and payment release authorizations - are often processed asynchronously. When an agent calls the endpoint to create a refund, Mollie does not immediately return a `status: "refunded"`. It returns `status: "queued"` or `status: "pending"`.

LLMs struggle with asynchronous state verification. If an agent executes a refund and reads the immediate response, it might assume the refund failed because the state is not "completed". It may then attempt to execute the refund a second time, resulting in double-refunding a customer. You must carefully engineer tool boundaries so the agent understands standard transitional states in financial infrastructure.

### Strict Rate Limiting and Error Delegation
Mollie enforces strict rate limits to protect its financial infrastructure. A naive AI agent iterating through thousands of invoices to perform a programmatic audit will quickly exhaust these limits, receiving HTTP 429 Too Many Requests responses.

Truto takes a strict architectural stance here: **we do not retry, throttle, or apply backoff on rate limit errors.** When Mollie returns an HTTP 429, Truto passes that error directly to the caller, normalizing the upstream rate limit info into standardized IETF headers (`ratelimit-limit`, `ratelimit-remaining`, `ratelimit-reset`). 

This is a critical safety feature for autonomous systems. If an infrastructure layer silently absorbs 429s and retries on behalf of an agent, the LLM continues executing logic based on stale timing assumptions, leading to race conditions in financial ledgers. The caller - your agent orchestration loop - must be responsible for reading these headers and executing deterministic retry/backoff logic. We go into more detail on this in our technical deep dive on [handling API rate limits and webhooks from dozens of integrations](https://truto.one/handling-api-rate-limits-and-webhooks-from-dozens-of-integrations/).

## Architecting a Unified Tool Layer for Mollie

Direct API tools - mapping one tool per raw Mollie endpoint - look convenient but push all provider quirks into the LLM's context window. The model has to remember exactly how to format the HAL links, exactly which ID string matches which resource (e.g., `tr_` for payments, `re_` for refunds), and exactly what fields are required to draft a sales invoice.

Truto collapses this complexity by translating the Mollie API into standardized Proxy APIs. This architecture aligns with the core principles of [the best unified APIs for LLM function calling and AI agent tools](https://truto.one/the-best-unified-apis-for-llm-function-calling-ai-agent-tools-2026/), where every integration on Truto maps to a comprehensive JSON schema defining `Resources` (like settlements or invoices) and `Methods` (like List, Get, Create). 

We provide these definitions directly to your LLM framework by calling the `/integrated-account/:id/tools` endpoint on the Truto API. This returns all available Proxy APIs with precise descriptions and strict JSON schemas, creating deterministic tools that LLM frameworks can consume instantly.

This gives your agent three concrete safety wins:
1. **Elimination of hallucinated arguments.** Every tool has a strict JSON schema. Invalid arguments are rejected before they hit the financial system.
2. **Abstracted pagination.** Truto handles the complex HAL pagination underneath. The agent simply requests data, and Truto manages the cursor traversal.
3. **Customizable agent instructions.** Truto allows you to edit the descriptions of every tool directly in the UI. If your agent is misusing the refund tool, you can update the description to explicitly state "Only use this tool after verifying the payment status is 'paid'" - and the `/tools` endpoint updates in real-time.

## Essential Mollie AI Agent Tools

When connecting Mollie to AI agents, you should restrict the model's access to high-leverage operations. Do not expose destructive tools unless strictly necessary. Below are the core hero tools you can fetch via Truto to build powerful FinOps agents.

### list_all_mollie_settlements
Reconciling payouts is a manual burden for finance teams. This tool lists all Mollie settlements, allowing an agent to view the status, amounts, and periods of funds paid out to your bank account.

**Usage note:** Use this in combination with `list_all_mollie_settlement_payments` to allow an agent to build a complete reconciliation report for a given month.

> "Fetch all Mollie settlements from the last 30 days and cross-reference them against our internal ledger to ensure all expected payouts were deposited."

### list_all_mollie_payments
Retrieves a paginated list of all Mollie payments created by your organization. This is the foundational tool for support and billing agents needing to verify customer transactions.

**Usage note:** The response includes `amountRefunded` and `amountRemaining`, which the agent must check before attempting to initiate a new refund.

> "Find the recent Mollie payment for customer email john@example.com and tell me if the status is paid, failed, or expired."

### create_a_mollie_payment_refund
Creates a refund for a specific Mollie payment. The refunded amount is credited back to the customer via bank transfer, credit card, or original payment method.

**Usage note:** This tool requires a valid `payment_id`. Ensure your agent prompt instructs it to fetch the payment ID first using the list payments tool before attempting to issue a refund. 

> "The customer requested a refund for order #9942. Find the associated Mollie payment ID, verify it hasn't been refunded yet, and issue a partial refund of €15.00."

### create_a_mollie_sales_invoice
Creates a new Mollie sales invoice to send directly to customers. The agent can construct line items, calculate VAT amounts, and set the currency dynamically.

**Usage note:** The agent must provide a structured array of `lines` and a `recipientIdentifier`. It is highly recommended to test this tool in a sandbox environment to ensure the LLM correctly structures VAT data.

> "Draft a new Mollie sales invoice for Acme Corp. Include two line items: 'Consulting Retainer' for €1000 and 'Software License' for €250. Keep the invoice in draft status for my review."

### list_all_mollie_balances
Lists all Mollie balances including the primary balance, showing available and pending amounts across different currencies.

**Usage note:** Excellent for treasury agents that monitor liquidity and alert finance teams when available balances drop below a specific operational threshold.

> "Check our primary Mollie balance. If the available amount is over €50,000, trigger the workflow to sweep the excess funds to our corporate treasury account."

### create_a_mollie_payout
Creates a manual payout in Mollie from one of your balances to its configured bank account. 

**Usage note:** This is a consequential financial operation. In production, this tool should be placed behind a Human-in-the-Loop (HITL) approval step within your orchestration framework before execution.

> "Initiate a payout of €10,000 from our primary balance to the operational bank account to cover this week's vendor expenses."

To view the complete schema definitions and the full list of available tools - including chargeback management, customer mandates, and payment link generation - visit the [Mollie integration page](https://truto.one/integrations/detail/mollie).

## Workflows in Action

Once your agent has access to these normalized tools, you can string together autonomous workflows that replace hours of manual FinOps labor. Here is how specific personas use these capabilities in production.

### 1. Automated Refund Verification and Execution (Support Agent)
Support teams waste significant time tabbing between helpdesks and Mollie to process refunds.

> "Customer ticket #4412 states they were double-charged for their subscription. Check their payment history, confirm the duplicate charge, and refund the secondary transaction."

**Tool Execution Sequence:**
1. `list_all_mollie_customers` to find the customer ID based on the email provided in the ticket.
2. `list_all_mollie_customer_payments` to retrieve the recent payment history.
3. The agent analyzes the timestamps and amounts, identifying two identical charges within seconds of each other.
4. `create_a_mollie_payment_refund` targeting the ID of the secondary payment.
5. The agent replies to the ticket confirming the refund has been queued.

### 2. Settlement Reconciliation (FinOps Agent)
Finance teams struggle to unpack bulk settlements to map individual transactions to their internal ledger.

> "Reconcile yesterday's settlement stl_12345. List all captures and refunds deducted from it, and summarize the net payout vs the expected initial amount."

**Tool Execution Sequence:**
1. `get_single_mollie_settlement_by_id` to retrieve the top-level settlement amount and status.
2. `list_all_mollie_settlement_payments` to pull all successful incoming funds.
3. `list_all_mollie_settlement_refunds` to pull all deducted refunds.
4. `list_all_mollie_settlement_chargebacks` to capture any forced reversals.
5. The agent calculates the net values and formats a markdown summary proving the math matches the final settlement amount.

### 3. Invoice Generation and Delivery (Sales Ops Agent)
Sales teams often need to generate manual invoices for custom enterprise contracts outside the standard billing flow.

> "Generate a sales invoice for the newly signed enterprise contract with TechFlow Inc. They need to be billed €12,000 for the annual software license and €3,000 for implementation services. Set the due date to Net-30."

**Tool Execution Sequence:**
1. `list_all_mollie_customers` to verify TechFlow Inc exists and retrieve their `customerId`.
2. `create_a_mollie_sales_invoice` formatting the two specific line items, calculating the VAT correctly, and setting the `dueAt` parameter to 30 days from today.
3. The agent returns the generated PDF download link from the `_links` object for the sales rep to review.

## Building Multi-Step Workflows

To build these workflows, you need to bind Truto's tools to your LLM and handle the execution loop. Because Truto's `/tools` endpoint returns tools in standard formats, they are natively compatible with LangChain, Vercel AI SDK, and CrewAI.

Crucially, your agent loop must handle rate limits. Because Truto passes HTTP 429 errors directly to the caller with exact IETF headers, your agent architecture is responsible for catching the error, reading the `ratelimit-reset` header, and pausing execution before retrying.

Here is how you orchestrate this flow safely:

```mermaid
sequenceDiagram
    participant App as Your App / Framework
    participant Agent as LLM Orchestrator
    participant Truto as Truto Proxy Layer
    participant Mollie as Mollie API

    App->>Truto: GET /integrated-account/<id>/tools
    Truto-->>App: Returns JSON Tool Schemas
    App->>Agent: Bind tools to LLM (e.g., .bindTools())
    
    App->>Agent: Prompt: "Find and refund payment tr_890"
    Agent->>App: Tool Call: create_a_mollie_payment_refund(id="tr_890")
    App->>Truto: POST /proxy/mollie/refunds (mapped by Truto)
    Truto->>Mollie: POST /v2/payments/tr_890/refunds
    
    alt Rate Limit Exceeded
        Mollie-->>Truto: HTTP 429 Too Many Requests
        Truto-->>App: HTTP 429 (ratelimit-reset: 10)
        App->>App: Pause execution for 10 seconds
        App->>Truto: Retry POST /proxy/mollie/refunds
    end
    
    Mollie-->>Truto: HTTP 201 Created
    Truto-->>App: Normalized JSON Response
    App->>Agent: Return Tool Result (Success)
    Agent-->>App: "Refund successfully queued."
```

Using the `truto-langchainjs-toolset`, you can instantiate these tools programmatically. Here is a TypeScript example demonstrating how to initialize the tools, bind them to an OpenAI model, and construct an agent capable of executing Mollie tasks while respecting potential execution failures.

```typescript
import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor, createToolCallingAgent } from "langchain/agents";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { TrutoToolManager } from "truto-langchainjs-toolset";

// 1. Initialize the Truto Tool Manager with your API key and Mollie account ID
const trutoManager = new TrutoToolManager({
  apiKey: process.env.TRUTO_API_KEY!,
  integratedAccountId: process.env.MOLLIE_ACCOUNT_ID!,
});

async function runMollieFinOpsAgent() {
  // 2. Fetch all available proxy tools for the Mollie integration
  const tools = await trutoManager.getTools();
  
  // 3. Initialize the LLM
  const llm = new ChatOpenAI({ 
    modelName: "gpt-4o",
    temperature: 0,
  });

  // 4. Define the prompt template with explicit instructions
  const prompt = ChatPromptTemplate.fromMessages([
    ["system", `You are an elite autonomous FinOps agent connected to Mollie.
    You have the ability to read financial records and execute refunds or payouts.
    
    CRITICAL INSTRUCTIONS:
    - Always verify a payment's status before attempting a refund.
    - If a tool returns an error, analyze the error message. If it is a rate limit 
      issue (429), the system will handle backoff, but you must ensure your logic 
      remains consistent upon retry.
    - Never execute a payout without explicitly stating the exact amount and destination.`],
    ["human", "{input}"],
    ["placeholder", "{agent_scratchpad}"],
  ]);

  // 5. Create the tool-calling agent
  const agent = createToolCallingAgent({
    llm,
    tools,
    prompt,
  });

  // 6. Execute the agent with the user prompt
  const agentExecutor = new AgentExecutor({
    agent,
    tools,
    maxIterations: 10,
  });

  console.log("Executing FinOps workflow...");
  const result = await agentExecutor.invoke({
    input: "We received an escalation for john@example.com. Find his most recent payment and issue a full refund immediately."
  });

  console.log(result.output);
}

runMollieFinOpsAgent().catch(console.error);
```

When writing your production execution loop, ensure your HTTP client wraps tool invocations in a retry block that explicitly checks for `error.response.status === 429` and reads the `ratelimit-reset` header to pause execution. Truto guarantees that you receive the exact rate limit reality of the upstream API, ensuring your agent never operates on false assumptions of success.

## Moving Past Manual Financial Operations

Giving AI agents access to your Mollie environment unlocks autonomous revenue operations that previously required dedicated headcount. By using a unified proxy layer, you protect your system from hallucinated JSON payloads, isolate the LLM from complex HAL pagination, and maintain strict control over rate limit execution.

Instead of burning engineering cycles reading Mollie API docs to hand-code 40 different REST wrappers, you can fetch AI-ready tools dynamically and focus purely on agent orchestration and prompt engineering.

> Ready to connect your AI agents to Mollie and 100+ other SaaS APIs? We provide auto-generated, strictly typed tools that stop hallucinations before they happen.
>
> [Talk to us](https://cal.com/truto/partner-with-truto)