Lago MCP Server for CrewAIGive CrewAI instant access to 12 tools to Apply Coupon, Batch Events, Create Billable Metric, and more

Name: Lago
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Connect your CrewAI agents to Lago through Vinkius, pass the Edge URL in the `mcps` parameter and every Lago tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The Lago MCP Server for CrewAI is a standout in the Money Moves category — giving your AI agent 12 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Lago Specialist",
    goal="Help users interact with Lago effectively",
    backstory=(
        "You are an expert at leveraging Lago tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Lago "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Ed25519Audit chain

<40msKill switch

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 Lago MCP Server

Connect Lago to your AI agent to automate your metering and billing infrastructure. Lago is the open-source alternative to Stripe Billing, designed for complex usage-based pricing models.

When paired with CrewAI, Lago becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Lago tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Customer Management — Create and update customer profiles with upsert_customer and retrieve details with get_customer.
Subscription Lifecycle — Assign plans to customers using create_subscription and monitor them with get_subscription.
Billing Infrastructure — Define billing plans with create_plan and set up create_billable_metric to track consumption.
Usage Tracking — Send real-time usage data with send_event or batch_events to trigger accurate billing.
Financial Operations — Manage wallets, apply coupons, and list invoices to keep your revenue operations running smoothly.

The Lago MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 12 Lago tools available for CrewAI

When CrewAI connects to Lago through Vinkius, your AI agent gets direct access to every tool listed below — spanning usage-based-billing, metering, saas-metrics, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

apply

Apply coupon on Lago

Apply a coupon to a customer

batch

Batch events on Lago

Send a batch of usage events

create

Create billable metric on Lago

Create a billable metric

create

Create coupon on Lago

Create a coupon

create

Create plan on Lago

Create a new billing plan

create

Create subscription on Lago

Assign a plan to a customer (create subscription)

create

Create wallet on Lago

Create a wallet for prepaid credits

get

Get customer on Lago

Retrieve a customer by external ID

get

Get subscription on Lago

Retrieve a subscription by external ID

list

List invoices on Lago

List all invoices

send

Send event on Lago

Send a usage event

upsert

Upsert customer on Lago

Requires an external_id. Create or update a customer in Lago

Connect Lago to CrewAI via MCP

Follow these steps to wire Lago into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 12 tools from Lago

Why Use CrewAI with the Lago MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Lago through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Lago + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Lago MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Lago for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Lago, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Lago tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Lago against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Lago in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Lago immediately.

"Create a new customer in Lago with external ID 'user_123' and email 'dev@example.com'."

"Show me the subscription details for ID 'sub_98765'."

"List all invoices for my Lago account."

Troubleshooting Lago MCP Server with CrewAI

Common issues when connecting Lago to CrewAI through Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Lago + CrewAI FAQ

Common questions about integrating Lago MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.