Can I see my current MRR through the agent?

Yes! Use the `get_mrr_metrics` tool with a start and end date. The agent will return the MRR data for that period directly from ChartMogul.

How do I check the subscription status of a specific customer?

First, use `list_mogul_customers` to find the customer's UUID, then use the `get_mogul_customer_details` tool to see their active plans and history.

Where do I find my ChartMogul API Key?

Log in to your ChartMogul account and go to **Settings > API Keys**. You can create and copy your API key from there.

ChartMogul MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: ChartMogul
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ChartMogul through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 ChartMogul "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in ChartMogul?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 ChartMogul MCP Server

Connect your ChartMogul account to any AI agent and take full control of your subscription analytics through natural conversation. Access real-time SaaS metrics like MRR, ARR, and Churn Rate.

Pydantic AI validates every ChartMogul tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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

Metrics Oversight — Retrieve all high-level subscription metrics (MRR, ARR, ARPA, ASP) natively
Growth Intelligence — Access detailed customer count and churn rate data flawlessly
Customer Deep-Dives — List and retrieve complete profiles for any customer in your database securely
Data Logistics — List and audit all configured data sources providing information to your account flawlessly
Revenue Analysis — Track MRR and ARR trends over specific timeframes directly within your workspace
System Verification — Verify API connectivity and account status using the built-in ping and diagnostic tools

The ChartMogul MCP Server exposes 8 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.

How to Connect ChartMogul to Pydantic AI via MCP

Follow these steps to integrate the ChartMogul MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from ChartMogul with type-safe schemas

Why Use Pydantic AI with the ChartMogul MCP Server

Pydantic AI provides unique advantages when paired with ChartMogul 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 ChartMogul integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ChartMogul connection logic from agent behavior for testable, maintainable code

ChartMogul + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the ChartMogul MCP Server delivers measurable value.

Type-safe data pipelines: query ChartMogul with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ChartMogul tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ChartMogul and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ChartMogul responses and write comprehensive agent tests

ChartMogul MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect ChartMogul to Pydantic AI via MCP:

get_arr_metrics

Retrieve Annualized Run Rate metrics

get_customer_count_metrics

Retrieve total customer count metrics over time

get_mogul_customer_details

Get detailed information for a specific customer

get_mrr_metrics

Retrieve Monthly Recurring Revenue metrics

get_subscription_metrics

Retrieve all high-level subscription metrics (MRR, ARR, etc)

list_mogul_customers

List all customers in ChartMogul

list_mogul_data_sources

List all data sources configured in the account

ping_mogul_api

Verify connectivity and authentication with the ChartMogul API

Example Prompts for ChartMogul in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with ChartMogul immediately.

"What is my total MRR for the last 3 months?"

"Show me details for customer UUID 'cust_123456'."

"Get my subscription metrics for 2024-01-01 to 2024-03-31."

Troubleshooting ChartMogul MCP Server with Pydantic AI

Common issues when connecting ChartMogul to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ChartMogul + Pydantic AI FAQ

Common questions about integrating ChartMogul MCP Server with Pydantic AI.

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 ChartMogul MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.