Mercury MCP Server for LangChainGive LangChain instant access to 8 tools to Get Account, Get Balance, List Accounts, and more
LangChain is the leading Python framework for composable LLM applications. Connect Mercury through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Mercury app connector for LangChain 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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"mercury-alternative": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Mercury, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Mercury through native MCP adapters. Connect 8 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 LangChain via MCP
Follow these steps to wire Mercury into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Mercury MCP Server
LangChain provides unique advantages when paired with Mercury through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Mercury MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Mercury queries for multi-turn workflows
Mercury + LangChain Use Cases
Practical scenarios where LangChain combined with the Mercury MCP Server delivers measurable value.
RAG with live data: combine Mercury tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Mercury, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Mercury tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Mercury tool call, measure latency, and optimize your agent's performance
Example Prompts for Mercury in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Mercury to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersMercury + LangChain FAQ
Common questions about integrating Mercury MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.