How do I get my Mercury API token?

Log in to your Mercury account at app.mercury.com. Go to **Settings → API Tokens** and click **Create Token**. Choose the token type (Read Only for balance checks, or Read and Write for managing recipients). Copy the token immediately — it will only be shown once. Paste it into the configuration field below, and you're ready. No code, no SDK, no webhooks.

Can my AI agent check my startup's runway instantly during a board meeting?

Absolutely. Just ask your AI agent 'What's my current balance across all Mercury accounts?' and it returns real-time balances for every account — checking, savings, and treasury — in seconds. No tab-switching, no logging in, no waiting for the dashboard to load.

What if I need to review recent transactions for month-end close?

Ask your AI agent to pull transactions from any date range. It retrieves each transaction with amount, counterparty, status, and date — perfect for reconciliation. You can filter by account, ask for Wire vs ACH breakdowns, or request only transactions above a specific threshold, all without exporting CSVs.

Is this suitable for companies with multiple Mercury accounts?

Yes. The integration retrieves data across all accounts linked to your API token — operating accounts, savings accounts, and treasury balances. Perfect for venture-backed startups managing multiple entities, holding companies, or finance teams tracking cash positions across subsidiaries.

Mercury MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Mercury
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

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.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Mercury?"
    )
    print(response)

asyncio.run(main())

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* 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 business banking account to your AI agent and manage your startup finances conversationally.

LlamaIndex agents combine Mercury tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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 Balances — Check real-time balances across all your Mercury accounts instantly from your AI agent.
Transaction History — Pull recent transactions filtered by date, amount, or counterparty to track spending patterns.
Recipient Management — List, create, and manage payment recipients for ACH and wire transfers.
Account Details — Retrieve routing numbers, account numbers, and account metadata programmatically.

The Mercury MCP Server exposes 10 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 Mercury to LlamaIndex via MCP

Follow these steps to integrate the Mercury 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 10 tools from Mercury

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

Mercury MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Mercury to LlamaIndex via MCP:

create_recipient

Required for sending payments. Create a new payment recipient

get_account

Get bank account details

get_transaction

Get transaction details

get_treasury_balance

Get treasury balance overview

list_accounts

List all Mercury bank accounts

list_payments

List sent payments

list_recipients

List payment recipients

list_statements

List bank statements

list_team_members

List all team members

list_transactions

List recent transactions

Example Prompts for Mercury in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Mercury immediately.

"What's the current balance on all my Mercury accounts?"

"Show me all transactions above $5,000 from this month."

"List all my payment recipients."

Troubleshooting Mercury MCP Server with LlamaIndex

Common issues when connecting Mercury to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Mercury + LlamaIndex FAQ

Common questions about integrating Mercury MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Mercury tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.