How to Use the ChartMogul MCP in Pydantic AI

Q: How do I register the ChartMogul MCP toolset in Pydantic AI?

You use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your agent's toolsets list to auto-discover tools like getsummarymetrics.

Build type-safe billing agents by validating ChartMogul data at runtime with Pydantic AI.

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Connect ChartMogul MCP to Pydantic AI

Create your Vinkius account to connect ChartMogul to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup ChartMogul with Pydantic AI

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Type-safe SaaS metrics extraction with Pydantic AI

When your agent pulls revenue metrics, you cannot afford silent validation failures. This MCP Server ensures that every response from `get_mrr_history` and `get_arr_history` is parsed and validated against concrete Python types. If the billing API returns an unexpected data structure, the framework raises a validation error immediately. This keeps your financial reports accurate and prevents corrupted numbers from slipping into your database.

Safe customer profile lookups

Fetching customer profiles using `get_customer_details` and `get_customer_ltv` requires strict schema enforcement. Pydantic AI guarantees that data fields like email addresses and lifetime value numbers are formatted correctly before the agent processes them. You can also list your entire user base using `list_customers` or check billing structures with `list_subscription_plans`. Every object is validated against your Pydantic schemas at runtime.

Validated customer record creation

Writing data back to your dashboard requires absolute precision. When your agent runs `create_customer_record`, the framework validates the input payload before sending it over the wire. You can check the connection status using `get_api_status` and retrieve your sources with `list_data_sources`. This ensures your agent is operating on a verified connection before attempting any database modifications.

Setup guide

Set up ChartMogul MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "chartmogul-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to ChartMogul tools.",
)

result = await agent.run("List recent ChartMogul transactions")
print(result.output)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ChartMogul. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about ChartMogul MCP in Pydantic AI

You use the unified `MCPToolset` class pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your agent's `toolsets` list to auto-discover tools like `get_summary_metrics`.

The framework will fail loudly with a validation error at runtime. This prevents your agent from making decisions based on malformed or hallucinated metrics like incorrect `get_churn_rates` values.

Yes, it supports both Streamable HTTP and SSE transports. You just need to run the server externally and connect the toolset to your Vinkius endpoint.

Yes, the framework is completely model-agnostic. You can run these tools with local LLMs or any commercial provider while keeping the same strict runtime validation.

Vinkius executes the MCP server in a zero-trust, ephemeral V8 sandbox that deletes your session data instantly upon completion. Your raw subscription metrics pass through a single secure endpoint token, ensuring no data is ever cached or exposed.

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