Railz MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Railz 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
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 Railz. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Railz?"
)
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 Railz MCP Server
Empower your AI agent to access normalized financial data from various accounting, banking, and commerce platforms with Railz. By connecting Railz to your agent, you transform complex financial auditing into a natural conversation. Your agent can instantly list businesses, audit invoices, and retrieve deep financial reports like Balance Sheets and P&L statements without you ever touching a dashboard. Whether you are managing multiple client accounts or a single corporate entity, your agent acts as a real-time financial analyst, ensuring your data is always accessible and structured.
LlamaIndex agents combine Railz 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
- Business Directory — List all registered businesses in your Railz account and inspect their specific metadata.
- Connection Auditing — Monitor active service provider connections (QuickBooks, Xero, Shopify, etc.) for any business entity.
- Financial Reporting — Fetch real-time, normalized reports including Balance Sheets, Profit & Loss, and Cash Flow statements.
- Invoice & Bill Management — Query and analyze accounts receivable (invoices) and accounts payable (bills) across multiple providers.
- Commerce Insights — Access sales orders and transaction data from connected commerce platforms to track revenue trends.
The Railz 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 Railz to LlamaIndex via MCP
Follow these steps to integrate the Railz 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 Railz
Why Use LlamaIndex with the Railz MCP Server
LlamaIndex provides unique advantages when paired with Railz through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Railz tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Railz tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Railz, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Railz tools were called, what data was returned, and how it influenced the final answer
Railz + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Railz MCP Server delivers measurable value.
Hybrid search: combine Railz real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Railz 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 Railz for fresh data
Analytical workflows: chain Railz queries with LlamaIndex's data connectors to build multi-source analytical reports
Railz MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Railz to LlamaIndex via MCP:
get_balance_sheet
Get balance sheet report
get_business
Get details for a specific business
get_cash_flow
Get cash flow statement
get_profit_and_loss
Get profit and loss report
list_accounts
List chart of accounts
list_bank_transactions
List bank transactions
list_bills
List bills for a business
list_businesses
List all businesses in Railz
list_commerce_orders
g., Shopify, BigCommerce). List commerce orders
list_connections
g., QuickBooks, Xero) for a given business. List connections for a business
list_invoices
List invoices for a business
list_journal_entries
List journal entries
Example Prompts for Railz in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Railz immediately.
"List all businesses registered in my Railz account."
"Show active connections for business 'biz_123'."
"Get the Profit and Loss report for connection 'conn_456'."
Troubleshooting Railz MCP Server with LlamaIndex
Common issues when connecting Railz to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRailz + LlamaIndex FAQ
Common questions about integrating Railz 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 Railz 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 Railz to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.