Can I test money flows safely without losing real funds?

Absolutely. Increase has a top-tier sandbox environment. In your MCP settings, pass a Sandbox API Key (starting with `sk_test_`) and optionally set the environment to 'sandbox'. We even implemented literal tool overrides like `increase_simulate_inbound_ach` which are intentionally restricted exclusively to simulating inbound events on your test environment.

Does adding a new account via the Agent require paperwork?

No! Assuming your global LLC has already passed initial onboarding and your Entity object handles compliance, any subsequent programmatic calls to `increase_create_account` yield a fully compliant sub-ledger and associated numbers instantaneously contextually within the UI chat responses.

What is the difference between ACH and Wire transfer arrays here?

ACH acts through clearing houses taking normally 1-3 business days suited perfectly for non-urgent vendor mass payouts (`create_ach`). Wire transfers (`create_wire`) tie directly to Fedwire routing logic hitting banks internally within minutes - often bearing a slight static fee. Dictate which the bot should invoke based on liquidity needs.

How can I trace a specific payment?

We've added `increase_list_transactions` to handle historical booked transfers strictly by ID limits, exposing whether money cleared Fed limits safely.

Increase MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Increase
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Increase as an MCP tool provider through 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 Increase. "
            "You have 12 tools available."
        ),
    )

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

asyncio.run(main())

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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 Increase MCP Server

The Increase MCP Server connects AI to a physical, fully compliant commercial US bank built explicitly top-down for programmatic transactions.

LlamaIndex agents combine Increase tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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

Endless Provisioning — Instantly generate new live bank accounts increase_create_account acting as sub-ledgers, enabling separate balances.
Open Payment Rails — Need to inject funds into a supplier directly using native American payment streams? Use increase_create_ach or sweep high-value overnight increase_create_wire securely.
Simulation Environment — Use Sandbox arrays to trigger simulated money hits increase_simulate_inbound_ach checking if an external agent script validates receiving deposits before going to production.

The Increase MCP Server exposes 12 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 Increase to LlamaIndex via MCP

Follow these steps to integrate the Increase 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 12 tools from Increase

Why Use LlamaIndex with the Increase MCP Server

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

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

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

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

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

Increase + LlamaIndex Use Cases

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

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

Data enrichment: query Increase 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 Increase for fresh data

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

Increase MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Increase to LlamaIndex via MCP:

increase_create_account

Spin up a new Bank Account programmatically

increase_create_ach

Push an outbound ACH transfer to any US Bank

increase_create_card

Issue a physical/virtual debit card attached to an account

increase_create_routing_number

Generate new ABA routing & account number data

increase_create_wire

Send a same-day US Wire transfer

increase_get_balance

Fetch realtime ledger balance for a specific account

increase_list_accounts

List all sub-accounts under your charter

increase_list_cards

Sweep the active array of issued Cards

increase_list_transactions

Financial history extraction (Booked)

increase_list_transfers

Audit outbound transfers

increase_simulate_inbound_ach

Simulate receiving an ACH inbound (SANDBOX ONLY)

increase_simulate_inbound_wire

Simulate receiving a Wire inbound (SANDBOX ONLY)

Example Prompts for Increase in LlamaIndex

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

"Use our main test routing sandbox mechanism to simulate inbound an external payload of $1000 into Account ID 'acc_1234'."

"Audit our entire open accounts layout right now."

"Spin up a new fresh physical corporate banking account dedicated uniquely to 'Server Spends'. Send the Routing number to me."

Troubleshooting Increase MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Increase + LlamaIndex FAQ

Common questions about integrating Increase 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.

Can I combine MCP tools with vector stores?

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