Infracost MCP Server for LlamaIndexGive LlamaIndex instant access to 13 tools to Create Guardrail, Create Price Book, Delete Guardrail, and more

Name: Infracost
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Infracost 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 Infracost MCP Server for LlamaIndex is a standout in the Loved By Devs category — giving your AI agent 13 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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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 Infracost. "
            "You have 13 tools available."
        ),
    )

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

asyncio.run(main())

$Infracost$

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 Infracost MCP Server

Connect Infracost to your AI agent to bring financial visibility and governance to your Infrastructure as Code (IaC) workflows. Prevent cloud cost surprises before they happen.

LlamaIndex agents combine Infracost tool responses with indexed documents for comprehensive, grounded answers. Connect 13 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

Cost Guardrails — List, create, and manage guardrails that trigger alerts or block Pull Requests when cost increases exceed your defined thresholds.
Tagging Governance — Enforce organizational standards by updating tagging policies, ensuring all cloud resources are correctly attributed to departments or projects.
Custom Pricing — Manage enterprise-specific price books for AWS and Azure to ensure cost estimates reflect your actual negotiated discounts.
Business Context — Upload custom properties from external systems like ServiceNow or Backstage to map cloud costs to your internal organizational hierarchy.
Pricing Queries — Programmatically query cloud pricing data to compare costs across different resource types and regions.

The Infracost MCP Server exposes 13 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 13 Infracost tools available for LlamaIndex

When LlamaIndex connects to Infracost through Vinkius, your AI agent gets direct access to every tool listed below — spanning terraform, cloud-cost, finops, 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.

create

Create guardrail on Infracost

Create a new guardrail

create

Create price book on Infracost

Create a custom price book for an enterprise

delete

Delete guardrail on Infracost

Delete a guardrail

get

Get guardrail on Infracost

Get a specific guardrail by ID

get

Get price book on Infracost

Poll this to wait for SUCCESS status. Get the status and details of a price book

get

Get price book files url on Infracost

Use the returned URL to PUT the CSV file. Get a presigned URL to upload a price book CSV

ingest

Ingest price book on Infracost

Call this after uploading the CSV to the presigned URL. Trigger ingestion of an uploaded price book CSV

list

List guardrails on Infracost

List all guardrails for an organization

query

Query pricing on Infracost

Provide a valid GraphQL query string. Query the Cloud Pricing API using GraphQL

update

Update guardrail on Infracost

Update an existing guardrail

update

Update organization on Infracost

Pass the customPriceBookId to activate it. Update organization settings, such as activating a custom price book

update

Update tagging policy on Infracost

Update allowed tag values for a tagging policy

upload

Upload custom properties on Infracost

g., from ServiceNow or Backstage) for cost attribution. Provide raw CSV data. Upload custom business properties via CSV

Connect Infracost to LlamaIndex via MCP

Follow these steps to wire Infracost 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 13 tools from Infracost

Why Use LlamaIndex with the Infracost MCP Server

LlamaIndex provides unique advantages when paired with Infracost through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Infracost tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Infracost tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Infracost, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Infracost tools were called, what data was returned, and how it influenced the final answer

Infracost + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Infracost MCP Server delivers measurable value.

Hybrid search: combine Infracost real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Infracost 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 Infracost for fresh data

Analytical workflows: chain Infracost queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Infracost in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Infracost immediately.

"List all cost guardrails for the organization 'acme-corp'."

"Create a new guardrail for 'acme-corp' that triggers when cost increase exceeds $200 and blocks the PR."

"Update the tagging policy for 'acme-corp' to make the 'Environment' tag mandatory."

Troubleshooting Infracost MCP Server with LlamaIndex

Common issues when connecting Infracost to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Infracost + LlamaIndex FAQ

Common questions about integrating Infracost 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 Infracost tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.