Bring Billing
to Pydantic AI
Learn how to connect Stigg 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 Stigg MCP Server?
Connect your Stigg account to any AI agent to take full control of your pricing and packaging workflows. Manage the entire customer lifecycle from provisioning to usage reporting through natural conversation.
What you can do
- Customer Lifecycle — Create, update, and retrieve customer profiles using REST or GraphQL tools.
- Subscription Management — Provision new subscriptions, fetch active plan details, or cancel them when needed.
- Usage Reporting — Report metered feature usage in real-time to ensure accurate billing and entitlement enforcement.
- Hybrid API Access — Choose between REST and GraphQL actions for flexible integration with your billing data.
How it works
- Subscribe to this server
- Enter your Stigg API Key
- Start managing your SaaS billing from Claude, Cursor, or any MCP-compatible client
No more jumping between dashboards to check a customer's entitlement or manually reporting usage. Your AI acts as a billing operations assistant.
Who is this for?
- Product Managers — instantly check customer plan statuses and feature entitlements without opening the Stigg console.
- Customer Success — provision trials or update customer details directly during support interactions.
- Developers — test billing flows and report usage for metered features straight from the terminal or IDE.
Built-in capabilities (12)
Get customer details via GraphQL
Get entitlements state via GraphQL
Provision a customer and optional subscription via GraphQL
Provision a subscription via GraphQL
Report usage via GraphQL
Cancel a subscription via REST API
Create a new customer via REST API
Create a subscription via REST API
Retrieve a customer via REST API
Retrieve a subscription via REST API
Report usage for metered features via REST API
Update a customer via REST API
Why Pydantic AI?
Pydantic AI validates every Stigg 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 Stigg integration code
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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 Stigg connection logic from agent behavior for testable, maintainable code
Stigg in Pydantic AI
Stigg and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Stigg 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 Stigg in Pydantic AI
The Stigg 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
Stigg for Pydantic AI
Every tool call from Pydantic AI to the Stigg MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I provision a customer and a subscription in one go?
Yes! Use the gql_provision_customer tool. It allows you to create the customer entity and optionally provide a planId to start their subscription immediately in a single GraphQL call.
How do I report usage for a metered feature?
You can use either rest_report_usage or gql_report_usage. Simply provide the customerId, the featureId, and the numeric value to update their metered usage in Stigg.
Is it possible to cancel an active subscription via the agent?
Yes, use the rest_cancel_subscription tool by providing the specific subscription ID. The agent will process the cancellation through Stigg's REST API.
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 Stigg MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai
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