Brankas MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Brankas as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Brankas. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Brankas?"
)
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 Brankas MCP Server
Connect your Brankas Open Finance account to any AI agent and orchestrate your payments, payouts, and financial data workflows across Southeast Asia through natural conversation.
LlamaIndex agents combine Brankas tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Direct Payments (Money-in) — Initiate checkout sessions to accept instant account-to-account payments and track transaction statuses.
- Disbursements (Money-out) — Send payouts automatically through inter-bank or intra-bank transfers and monitor their progress.
- Data Aggregation — Retrieve real-time bank account balances, transaction history (statements), and identity data from consented users.
The Brankas MCP Server exposes 8 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 Brankas to LlamaIndex via MCP
Follow these steps to integrate the Brankas 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 8 tools from Brankas
Why Use LlamaIndex with the Brankas MCP Server
LlamaIndex provides unique advantages when paired with Brankas through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Brankas tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Brankas tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Brankas, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Brankas tools were called, what data was returned, and how it influenced the final answer
Brankas + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Brankas MCP Server delivers measurable value.
Hybrid search: combine Brankas real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Brankas 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 Brankas for fresh data
Analytical workflows: chain Brankas queries with LlamaIndex's data connectors to build multi-source analytical reports
Brankas MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Brankas to LlamaIndex via MCP:
create_checkout
Create a new Direct payment checkout session
get_balance
Retrieve linked bank account balances
get_identities
Retrieve linked identity data
get_statement
Retrieve linked bank statement data
get_transaction
Get status of a Direct payment transaction
get_transfer_status
Get status of a Disburse transfer
inter_bank_transfer
Initiate an inter-bank disbursement (payout)
intra_bank_transfer
Initiate an intra-bank disbursement (payout)
Example Prompts for Brankas in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Brankas immediately.
"Create a checkout session for 500 PHP."
"Check the status of transfer trf_99283."
"Retrieve the latest bank statement data."
Troubleshooting Brankas MCP Server with LlamaIndex
Common issues when connecting Brankas to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBrankas + LlamaIndex FAQ
Common questions about integrating Brankas 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?
Connect Brankas with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Brankas to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.