Mercury MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Get Account, Get Balance, List Accounts, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mercury 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 App Connector for LlamaIndex
The Mercury app connector for LlamaIndex is a standout in the Money Moves category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Mercury. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Mercury?"
)
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 Mercury MCP Server
Connect your Mercury banking account to any AI agent and manage startup finances through natural conversation.
LlamaIndex agents combine Mercury 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
- Account Management — Access balances across all checking and savings accounts
- Transactions — Browse and filter recent transactions and transfers
- Statements — Retrieve monthly account statements
- Cash Flow — Track incoming revenue and outgoing expenses
- Recipient Management — Access saved wire and ACH recipients
The Mercury 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.
All 8 Mercury tools available for LlamaIndex
When LlamaIndex connects to Mercury through Vinkius, your AI agent gets direct access to every tool listed below — spanning business-banking, financial-automation, transaction-reconciliation, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Get details for a specific Mercury account
Get account balance
List Mercury bank accounts
List Mercury debit cards
List invoicing customers
List account receivable invoices
List payment recipients
List transactions for an account
Connect Mercury to LlamaIndex via MCP
Follow these steps to wire Mercury into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Mercury MCP Server
LlamaIndex provides unique advantages when paired with Mercury through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mercury tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mercury tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mercury, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mercury tools were called, what data was returned, and how it influenced the final answer
Mercury + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mercury MCP Server delivers measurable value.
Hybrid search: combine Mercury real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mercury 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 Mercury for fresh data
Analytical workflows: chain Mercury queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Mercury in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mercury immediately.
"Show my current balances for all accounts."
"List all outgoing transactions over $1,000 from last week."
"Get total revenue received this month."
Troubleshooting Mercury MCP Server with LlamaIndex
Common issues when connecting Mercury to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMercury + LlamaIndex FAQ
Common questions about integrating Mercury MCP Server with LlamaIndex.