Are actions simulated or operating live on real Ledgers?

If you inject your core Project Access Token with full production permissions and leave the `env` string off `sandbox`, yes! It executes heavy Core Banking GraphQL mutations that immediately provision legally compliant EU ledgers behind the scenes.

Can the AI Agent manage physical or virtual credit cards?

Yes. Via `swan_add_virtual_card` and `swan_cancel_card` the LLM has complete programmatic control over debit and spend cards mapped to underlying accounts.

Does the system support sandbox incoming money simulation?

Yes! We expose the `swan_simulate_incoming_transfer` specifically so you can test your programmatic workflows in SandBox by pushing fake money into target ledgers.

Swan MCP Server for LlamaIndex 9 tools — connect in under 2 minutes

Name: Swan
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 9 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Swan. "
            "You have 9 tools available."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Swan MCP Server

The Swan MCP Server embeds a complete European Banking-as-a-Service architecture into Vinkius LLMs.

LlamaIndex agents combine Swan tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the 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

Automated Root Provisioning — Instantly spin up local branch operations allocating FRA or ESP IBAN formats through swan_create_account.
Card Administration — Ask the agent to generate custom virtual Mastercards assigned exclusively to distinct contractors utilizing swan_add_virtual_card.
Direct SEPA Execution — Move exact funds safely parsing external creditor data natively through swan_create_sepa_transfer directly across European networks.

The Swan MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Swan to LlamaIndex via MCP

Follow these steps to integrate the Swan MCP Server with LlamaIndex.

Install dependencies

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

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

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

Explore tools

The agent discovers 9 tools from Swan

Why Use LlamaIndex with the Swan MCP Server

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

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

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

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

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

Swan + LlamaIndex Use Cases

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

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

Data enrichment: query Swan 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 Swan for fresh data

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

Swan MCP Tools for LlamaIndex (9)

These 9 tools become available when you connect Swan to LlamaIndex via MCP:

swan_add_virtual_card

Provisions a robust Mastercard Virtual Debit

swan_cancel_card

Permanently cancel a specific corporate card

swan_create_account

Requires an existing AccountHolderId. Dynamically provision a European Account under your ledger

swan_create_sepa_transfer

Initiate a standard European SEPA Credit Transfer

swan_get_accounts

List all operational Swan Bank Accounts/IBANs

swan_get_project_info

Fetch overarching details about your connected Swan Project Node

swan_get_transactions

Retrieve the ledger history for a specific Account

swan_list_cards

List all physical and virtual cards

swan_simulate_incoming_transfer

Sandbox Only - Inject fake money

Example Prompts for Swan in LlamaIndex

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

"Retrieve my core project identifier and map the legal entity ID."

"Launch a brand new sub-account in France. Bind it to the root entity targeting EUR processing."

"Sweep the ledger of Account X123 and list the latest 5 transactions."

Troubleshooting Swan MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Swan + LlamaIndex FAQ

Common questions about integrating Swan MCP Server with LlamaIndex.

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.