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How to Use the Infracost MCP in LlamaIndex

Q: How does LlamaIndex index Infracost guardrails?

The agent calls listguardrails to retrieve all active cost policies from the server. It then converts this structural data into document nodes and indexes them into your vector store for semantic retrieval.

Q: Can LlamaIndex query custom Infracost price books?

Yes. After you run ingestpricebook to load your enterprise rates, LlamaIndex queries them using querypricing to ground its cost calculations in your actual contract terms.

Q: What setup is required for the Infracost MCP Server in LlamaIndex?

Install llama-index-tools-mcp and configure the BasicMCPClient with your Vinkius server URL. Use McpToolSpec to load the tools and pass them to your FunctionAgent.

Q: How does the agent track the status of a price book ingestion?

The agent polls getpricebook repeatedly until the status returns success. Once confirmed, it applies the updated rates to your organization settings.

Build a searchable, cost-aware knowledge base by indexing Infracost pricing data directly into LlamaIndex vector stores.

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Connect Infracost MCP to LlamaIndex

Create your Vinkius account to connect Infracost to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Infracost with LlamaIndex

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Grounding Agent Decisions in Real Price Data

Your LlamaIndex agent executes `query_pricing` via the MCP server to fetch live cloud costs and feeds the output directly into your index. This prevents your agent from hallucinating old pricing numbers when suggesting architectural changes. By storing these query results in a vector database, your RAG pipeline answers cost questions based on real, current API data. You get highly accurate estimates without making repetitive external API calls.

Indexing Cost Guardrails via MCP Server

This MCP Server exposes `list_guardrails` to let LlamaIndex ingest your active budget policies into its semantic index. The agent maps these rules so developers can search for active budget limits using natural language. When a policy changes, the agent runs `get_guardrail` to update the index instantly. This keeps your search results aligned with actual organizational constraints.

Ingesting Custom Price Books into RAG

Your LlamaIndex agent initiates custom rate ingestion by calling `create_price_book` to handle enterprise-specific discount structures. The agent uploads the rate CSV using `get_price_book_files_url` and monitors progress with `get_price_book`. Once the book is active, the agent runs `update_organization` to apply these rates across your queries. Your RAG system now answers cost questions using your actual negotiated prices instead of public retail rates.

Setup guide

Set up Infracost MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Infracost MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Infracost tools.",
)
response = await agent.run("List recent Infracost data")

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Common questions about Infracost MCP in LlamaIndex

The agent calls `list_guardrails` to retrieve all active cost policies from the server. It then converts this structural data into document nodes and indexes them into your vector store for semantic retrieval.

Yes. After you run `ingest_price_book` to load your enterprise rates, LlamaIndex queries them using `query_pricing` to ground its cost calculations in your actual contract terms.

Install `llama-index-tools-mcp` and configure the `BasicMCPClient` with your Vinkius server URL. Use `McpToolSpec` to load the tools and pass them to your `FunctionAgent`.

The agent polls `get_price_book` repeatedly until the status returns success. Once confirmed, it applies the updated rates to your organization settings.

It only handles resource metadata, custom pricing CSVs uploaded via `ingest_price_book`, and tagging rules. Your actual cloud credentials and raw Terraform state files are never stored or exposed.

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