Column 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 Column 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 Column. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Column?"
)
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 Column MCP Server
The Column MCP Server effectively bypasses standard FinTech wrappers and ties your artificial intelligence directly to one of the only nationally chartered US banks built originally around raw Developer APIs.
LlamaIndex agents combine Column 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
- Automated Clearing — Use
column_create_ach_transferto reliably settle recurring vendor payouts directly out of your native balance without relying on external UI web panels. - Establish Corporate Entities — Hook your conversational bots to construct KYC/KYB verified operational clusters
column_create_entityready to map against newly minted bank account numbers (column_create_bank_account). - Physical Check Writing — Astonishing API feature: send literal paper checks natively out to US addresses. Formulate text like "Mail a $40 check to John's address in Texas for maintenance" and the
column_create_checkprints and bounds the ledger payload directly.
The Column 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 Column to LlamaIndex via MCP
Follow these steps to integrate the Column 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 Column
Why Use LlamaIndex with the Column MCP Server
LlamaIndex provides unique advantages when paired with Column through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Column tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Column tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Column, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Column tools were called, what data was returned, and how it influenced the final answer
Column + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Column MCP Server delivers measurable value.
Hybrid search: combine Column real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Column 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 Column for fresh data
Analytical workflows: chain Column queries with LlamaIndex's data connectors to build multi-source analytical reports
Column MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Column to LlamaIndex via MCP:
column_create_ach_transfer
Fire an ACH to an external routing/account number
column_create_bank_account
Establish a DDA (Demand Deposit Account)
column_create_check
Very useful for legacy vendor systems. Generate and mail a paper check
column_create_entity
In production, this goes through compliance screening. Register a business or person KYC target inside Column
column_create_wire_transfer
Fire an immediate Wire transfer
column_get_balance
Audit settled funds inside a Bank Account
column_get_bank_account
Fetch specific DDA details (Routing info)
column_get_statement
Retrieve the generated bank statement artifacts
column_list_entities
View all active KYC profiles under the charter
column_list_transfers
Sweep historical ACH payment operations
column_list_webhooks
View all registered listening streams
column_simulate_ach
Trigger Sandbox inbound money movement
Example Prompts for Column in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Column immediately.
"Scan our balance history within my Operational account ID. See exactly how much pure funds are settled and available for dispatch."
"Print out a $1,500 manual paper check paid out to 'Green Construction LLC'. Mail it to '55 Broad St, Chicago IL 60601'."
"Initialize a Same-Day direct ACH batch targeting our landlord accounting info. Execute a $5,000 push towards Counterparty Router 02844 under entity RentalCorp."
Troubleshooting Column MCP Server with LlamaIndex
Common issues when connecting Column to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpColumn + LlamaIndex FAQ
Common questions about integrating Column 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 Column 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 Column to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.