MoonClerk MCP Server for Pydantic AIGive Pydantic AI instant access to 7 tools to Get Customer, Get Form, Get Payment, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect MoonClerk 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 MoonClerk app connector for Pydantic AI is a standout in the Money Moves category — giving your AI agent 7 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 MoonClerk "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in MoonClerk?"
)
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 MoonClerk MCP Server
Connect your MoonClerk account to any AI agent to streamline your payment monitoring and customer oversight. MoonClerk provides a robust API for programmatically retrieving your payment records, customer plan details, and active payment forms.
Pydantic AI validates every MoonClerk tool response against typed schemas, catching data inconsistencies at build time. Connect 7 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
- Customer Monitoring — List all your customers (plans) and retrieve detailed metadata including status and plan types
- Payment Tracking — Access your full payment history and monitor the status of recurring and one-time transactions
- Form Orchestration — List all your active payment forms and retrieve their public URLs and configurations
- Coupon Intelligence — Access your list of active coupons to understand your promotional landscape
- Real-time Oversight — Get a comprehensive overview of your payment volume and customer growth using natural language commands
The MoonClerk MCP Server exposes 7 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 7 MoonClerk tools available for Pydantic AI
When Pydantic AI connects to MoonClerk through Vinkius, your AI agent gets direct access to every tool listed below — spanning recurring-payments, checkout-pages, subscription-management, 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 customer details
Get form details
Get payment details
List all coupons
List all MoonClerk customers
List all payment forms
List all payments
Connect MoonClerk to Pydantic AI via MCP
Follow these steps to wire MoonClerk 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 MoonClerk MCP Server
Pydantic AI provides unique advantages when paired with MoonClerk 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 MoonClerk integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your MoonClerk connection logic from agent behavior for testable, maintainable code
MoonClerk + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the MoonClerk MCP Server delivers measurable value.
Type-safe data pipelines: query MoonClerk with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple MoonClerk tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query MoonClerk and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock MoonClerk responses and write comprehensive agent tests
Example Prompts for MoonClerk in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with MoonClerk immediately.
"List all active customers in my MoonClerk account."
"Show the last 5 payments received today."
"List all my active payment forms."
Troubleshooting MoonClerk MCP Server with Pydantic AI
Common issues when connecting MoonClerk to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMoonClerk + Pydantic AI FAQ
Common questions about integrating MoonClerk 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.