How to Use the ChartMogul MCP in Pydantic AI

Validate live subscription revenue and MRR data with runtime safety inside Pydantic AI using this MCP Server.

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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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Enforce type-safe revenue checks with Pydantic AI

The `get_mrr_metrics` tool delivers monthly recurring revenue data directly into your Pydantic AI agent. Since the framework validates every response at runtime, your agent receives strictly typed float values rather than unvalidated API payloads. If the billing system returns unexpected null values, the Pydantic AI framework raises a validation error immediately. This prevents your agent from making decisions based on malformed financial data.

Validate customer schemas using this MCP Server

The `list_mogul_customers` tool queries your active subscriber database while enforcing strict schema validation. Your Pydantic AI agent parses the raw customer arrays into structured Python models before executing any logic. This setup guarantees that email fields, creation dates, and status strings are perfectly formatted. It stops hallucinated customer profiles from corrupting your internal databases.

Audit high-level SaaS metrics safely

The `get_subscription_metrics` tool fetches your complete SaaS revenue overview for type-safe processing. Your Pydantic AI agent uses this tool to ingest MRR, ARR, and active customer counts under strict runtime type checks. By wrapping this MCP tool in a dynamic schema, you ensure that any changes to your billing structure won't break your agent's decision trees silently. The system either validates perfectly or fails loudly.

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-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)

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Common questions about ChartMogul MCP in Pydantic AI

You use the MCPToolset class with your Vinkius HTTP endpoint and pass it to your Agent constructor. This registers all ChartMogul tools as type-safe MCP dependencies.

If the API returns a string where a float is expected, Pydantic AI raises a validation error. This prevents your agent from executing tasks with corrupt financial numbers.

Yes, because Pydantic AI is model-agnostic, you can use local models or commercial APIs to process your billing metrics via the managed MCP Server.

No, you should use the unified MCPToolset approach instead of the deprecated class. It simplifies the connection and handles tool discovery automatically.

Your subscription records, customer emails, and revenue data are encrypted in transit and processed within an isolated sandbox. Vinkius handles the API token authentication securely, ensuring no credentials are exposed to the LLM.

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