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

Name: OpenCost (K8s Cost)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OpenCost (K8s Cost) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The OpenCost (K8s Cost) MCP Server for LlamaIndex is a standout in the Cloud Infrastructure category — giving your AI agent 6 tools to work with, ready to go from day one.

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to OpenCost (K8s Cost). "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in OpenCost (K8s Cost)?"
    )
    print(response)

asyncio.run(main())

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* 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.

LlamaIndex agents combine OpenCost (K8s Cost) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

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 6 tools from OpenCost (K8s Cost)

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

LlamaIndex provides unique advantages when paired with OpenCost (K8s Cost) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine OpenCost (K8s Cost) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain OpenCost (K8s Cost) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query OpenCost (K8s Cost), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what OpenCost (K8s Cost) tools were called, what data was returned, and how it influenced the final answer

OpenCost (K8s Cost) + LlamaIndex Use Cases

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

Hybrid search: combine OpenCost (K8s Cost) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query OpenCost (K8s Cost) to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying OpenCost (K8s Cost) for fresh data

Analytical workflows: chain OpenCost (K8s Cost) queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for OpenCost (K8s Cost) in LlamaIndex

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

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

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

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

Troubleshooting OpenCost (K8s Cost) MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

OpenCost (K8s Cost) + LlamaIndex FAQ

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

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query OpenCost (K8s Cost) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.