Mercury MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Get Account, Get Balance, List Accounts, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mercury through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Mercury app connector for Pydantic AI 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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Mercury "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Mercury?"
)
print(result.data)
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.
Pydantic AI validates every Mercury tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI
When Pydantic AI 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 Pydantic AI via MCP
Follow these steps to wire Mercury into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Mercury MCP Server
Pydantic AI provides unique advantages when paired with Mercury through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Mercury integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Mercury connection logic from agent behavior for testable, maintainable code
Mercury + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Mercury MCP Server delivers measurable value.
Type-safe data pipelines: query Mercury with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Mercury tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Mercury and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Mercury responses and write comprehensive agent tests
Example Prompts for Mercury in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Mercury to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMercury + Pydantic AI FAQ
Common questions about integrating Mercury MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.