Bring Subscription Metrics
to Pydantic AI
Learn how to connect ProfitWell to Pydantic AI and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the ProfitWell MCP Server?
Connect your ProfitWell (Paddle Metrics) account to any AI agent and take full control of your subscription revenue orchestration through natural conversation. ProfitWell provides a world-class platform for subscription intelligence, and this integration allows you to retrieve real-time metrics (MRR, Churn, LTV), manage customer history, and update subscription data directly from your chat interface.
What you can do
- Metric & Revenue Orchestration — Retrieve monthly and daily subscription metrics programmatically to maintain a clear overview of your business growth.
- Customer Lifecycle Intelligence — Access and monitor detailed customer history, including subscription changes and churn events directly from the AI interface.
- Subscription & Manual Data Control — Create, update, and 'un-churn' subscriptions via natural language to keep your revenue data synchronized.
- Retention & LTV Monitoring — Retrieve retention metrics and customer lifetime value (LTV) metadata to ensure your unit economics are always optimized.
- Operational Monitoring — Track system health and manage plan metadata using simple AI commands.
How it works
- Subscribe to this server
- Enter your ProfitWell Private API Token from your integration settings
- Start managing your subscription metrics from Claude, Cursor, or any MCP-compatible client
No more manual exporting of CSV results for basic churn checks. Your AI acts as a dedicated revenue analyst or growth coordinator.
Who is this for?
- Founders & Growth Leads — quickly retrieve revenue summaries and monitor churn trends without switching apps.
- Finance Operations Teams — automate the retrieval of subscription metadata and track retention metrics via natural conversation.
- Customer Success Managers — streamline the retrieval of customer history and monitor account health directly within the chat.
Built-in capabilities (12)
Mark churn event
Add new subscriber
g. test accounts). Exclude from metrics
Get API status
Get billing history
Get Lifetime Value
Get daily growth stats
Get monthly financial stats
Get churn metrics
List product plans
Reverse a cancellation
Update subscription
Why Pydantic AI?
Pydantic AI validates every ProfitWell tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your ProfitWell integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your ProfitWell connection logic from agent behavior for testable, maintainable code
ProfitWell in Pydantic AI
ProfitWell and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect ProfitWell to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for ProfitWell in Pydantic AI
The ProfitWell 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. All 12 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
ProfitWell for Pydantic AI
Every tool call from Pydantic AI to the ProfitWell MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can my AI automatically find the MRR and churn metrics for the current month?
Yes! Use the get_monthly_metrics tool. Your agent will respond with complete metadata for all key metrics, including MRR, churned revenue, and active customers in seconds.
How do I find my ProfitWell Private API Token?
Log in to your ProfitWell/Paddle account, navigate to Account Settings > Integrations, and look for the ProfitWell API card to find your Private Token.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your ProfitWell MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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