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OpenCost (K8s Cost) MCP Server for CrewAIGive CrewAI instant access to 6 tools to Get Allocation, Get Assets, Get Cloud Cost, and more

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Connect your CrewAI agents to OpenCost (K8s Cost) through Vinkius, pass the Edge URL in the `mcps` parameter and every OpenCost (K8s Cost) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The OpenCost (K8s Cost) MCP Server for CrewAI is a standout in the Cloud Infrastructure category — giving your AI agent 6 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="OpenCost (K8s Cost) Specialist",
    goal="Help users interact with OpenCost (K8s Cost) effectively",
    backstory=(
        "You are an expert at leveraging OpenCost (K8s Cost) 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 OpenCost (K8s Cost) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 6 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
OpenCost (K8s Cost)
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
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 OpenCost (K8s Cost) MCP Server

Connect your OpenCost instance to any AI agent to gain real-time visibility into your Kubernetes spending and infrastructure efficiency through natural language.

When paired with CrewAI, OpenCost (K8s Cost) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call OpenCost (K8s Cost) 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

  • Workload Allocation — Query costs and resources allocated to clusters, nodes, namespaces, controllers, and pods using get_allocation.
  • Asset Inspection — Retrieve backing cost data for physical infrastructure like Nodes, Disks, and Load Balancers via get_assets.
  • Cloud Billing Integration — Access AWS CUR, Azure Export, and GCP Billing data directly with get_cloud_cost to reconcile K8s costs with provider bills.
  • Third-Party Costs — Track external service expenses (e.g., Datadog, MongoDB Atlas) using custom cost timeseries and total summary tools.
  • Granular Filtering — Aggregate data by labels, annotations, or service levels to understand exactly where your budget is going.

The OpenCost (K8s Cost) MCP Server exposes 6 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 6 OpenCost (K8s Cost) tools available for CrewAI

When CrewAI connects to OpenCost (K8s Cost) through Vinkius, your AI agent gets direct access to every tool listed below — spanning kubernetes, cost-optimization, cloud-billing, 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.

get

Get allocation on OpenCost (K8s Cost)

Query costs and resources allocated to Kubernetes workloads

get

Get assets on OpenCost (K8s Cost)

Retrieve backing cost data broken down by individual assets

get

Get cloud cost on OpenCost (K8s Cost)

Retrieve cloud cost data directly from cloud provider billing reports

get

Get custom cost timeseries on OpenCost (K8s Cost)

g., Datadog, MongoDB Atlas). Get samples of third-party service costs over time steps

get

Get custom cost total on OpenCost (K8s Cost)

Get summary of third-party costs over a window

set

Set log level on OpenCost (K8s Cost)

Change OpenCost log level at runtime

Connect OpenCost (K8s Cost) to CrewAI via MCP

Follow these steps to wire OpenCost (K8s Cost) into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install CrewAI

Run pip install crewai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
03

Customize the agent

Adjust the role, goal, and backstory to fit your use case
04

Run the crew

Run python crew.py. CrewAI auto-discovers 6 tools from OpenCost (K8s Cost)

Why Use CrewAI with the OpenCost (K8s Cost) MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with OpenCost (K8s Cost) through the Model Context Protocol.

01

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

02

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

03

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

04

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

OpenCost (K8s Cost) + CrewAI Use Cases

Practical scenarios where CrewAI combined with the OpenCost (K8s Cost) MCP Server delivers measurable value.

01

Automated multi-step research: a reconnaissance agent queries OpenCost (K8s Cost) for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

02

Scheduled intelligence reports: set up a crew that periodically queries OpenCost (K8s Cost), analyzes trends over time, and generates executive briefings in markdown or PDF format

03

Multi-source enrichment pipelines: chain OpenCost (K8s Cost) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

04

Compliance and audit automation: a compliance agent queries OpenCost (K8s Cost) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for OpenCost (K8s Cost) in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with OpenCost (K8s Cost) immediately.

01

"Show me the cost allocation for all namespaces over the last 7 days."

02

"What are the backing asset costs for our nodes today?"

03

"Get the total summary for third-party service costs for the current month."

Troubleshooting OpenCost (K8s Cost) MCP Server with CrewAI

Common issues when connecting OpenCost (K8s Cost) to CrewAI through Vinkius, and how to resolve them.

01

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
02

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
03

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
04

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.

OpenCost (K8s Cost) + CrewAI FAQ

Common questions about integrating OpenCost (K8s Cost) MCP Server with CrewAI.

01

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.
02

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.
03

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.
04

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.
05

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.

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