Bring Mrr Tracking
to LangChain
Learn how to connect ChartMogul to LangChain and start using 12 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the ChartMogul MCP Server?
Connect your ChartMogul account to any AI agent and take full control of your SaaS revenue intelligence and subscription monitoring workflows through natural conversation.
What you can do
- Revenue Orchestration — Retrieve real-time metrics for Monthly Recurring Revenue (MRR), Annual Run Rate (ARR), and Average Revenue Per Account (ARPA) programmatically
- Churn & Retention Intelligence — Monitor customer churn rates and LTV (Lifetime Value) metrics across custom time intervals to understand your business health in real-time
- Customer Lifecycle Management — List and manage your subscriber base programmatically, including retrieving detailed historical profiles and MRR contributions
- Infrastructure Monitoring — Access information about your connected data sources (Stripe, Braintree, etc.) and subscription plans to ensure high-fidelity billing oversight
- Trend Analysis — Query historical metrics over specific periods (day, week, month, quarter) to identify growth patterns and seasonal shifts directly through your agent
How it works
1. Subscribe to this server
2. Retrieve your API Key from your ChartMogul dashboard (Settings > API)
3. Start managing your SaaS performance from Claude, Cursor, or any MCP client
No more manual scrubbing through filtered charts or exporting CSVs for reporting. Your AI acts as your dedicated SaaS finance analyst and growth coordinator.
Who is this for?
- Founders & CEOs — instantly retrieve high-level revenue summaries and churn reports using natural language commands
- Finance Teams — monitor ARR trends and customer LTV without leaving your communication tools
- Data Analysts — automate the retrieval of structured SaaS metrics for internal reporting through simple AI queries
Built-in capabilities (12)
Add new customer
Check connection
Analyze ARR
Analyze retention
Monitor user growth
Get customer profile
Check Customer LTV
Analyze MRR
Get key SaaS metrics
List SaaS customers
) connected to ChartMogul. List connected sources
List billing plans
Why LangChain?
LangChain's ecosystem of 500+ components combines seamlessly with ChartMogul through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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The largest ecosystem of integrations, chains, and agents. combine ChartMogul MCP tools with 500+ LangChain components
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Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
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LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
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Memory and conversation persistence let agents maintain context across ChartMogul queries for multi-turn workflows
ChartMogul in LangChain
ChartMogul and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect ChartMogul to LangChain 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 | 3,400+ 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 ChartMogul in LangChain
The ChartMogul 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 LangChain 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
ChartMogul for LangChain
Every tool call from LangChain to the ChartMogul MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I find my ChartMogul API Key?
Log in to your account, navigate to Settings > API, and copy your unique secret key.
Are the MRR and ARR metrics real-time?
Yes! The metrics tools retrieve the most current calculations based on the data synced into your ChartMogul account.
Can I filter metrics by specific date ranges?
Absolutely. Use the get_summary_metrics tool and provide start-date and end-date parameters to analyze specific growth periods.
How does LangChain connect to MCP servers?
Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
Which LangChain agent types work with MCP?
All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
Can I trace MCP tool calls in LangSmith?
Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.
MultiServerMCPClient not found
Install: pip install langchain-mcp-adapters