How to Use the DoiT MCP in LlamaIndex

Q: Can LlamaIndex index my DoiT asset configurations?

Yes, the agent calls getassetdetails to fetch configuration metadata. This text is then parsed, embedded, and stored in your vector database. You can then query your infrastructure setup semantically.

Q: How does LlamaIndex handle DoiT budget alerts?

The framework query engine uses listexceededcostbudgets to check for overruns. It compares current spending against the limits retrieved by listcostbudgets. This data grounds the agent's response.

Q: Can I filter which tools the agent can access?

Yes. You can use the allowedtools filter when initializing the tool spec. This limits the agent to specific actions like reading budgets or listing assets.

Index live cloud cost data from DoiT into your LlamaIndex RAG pipelines to eliminate agent hallucinations.

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Connect DoiT MCP to LlamaIndex

Create your Vinkius account to connect DoiT 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 DoiT with LlamaIndex

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Ground your agent in real-time DoiT billing data

`get_billing_cost_summary` retrieves the high-level cloud spending metrics that your LlamaIndex agent indexes for semantic search. Your LLM won't have to guess your actual cloud spend. Instead, it queries the live API. The local vector index stores this output from the MCP connection. Asking about budget trends triggers the agent to pull these exact numbers for the answer.

Build a searchable index of your cloud assets

`list_cloud_assets` extracts the complete inventory of your AWS, GCP, and Azure resources. Ingesting this inventory directly into your LlamaIndex pipeline turns raw infrastructure data into searchable documents. To pinpoint a specific resource, the agent uses `search_cloud_assets` to narrow down the index. Your cloud footprint becomes queryable in plain English.

Query cost anomalies using this MCP Server

`list_cost_anomalies` identifies unexpected spending spikes and feeds them directly into your knowledge base. Monitoring these anomalies over time allows your agent to notice patterns that manual audits miss. Ask the agent why a specific bill jumped. The engine queries the indexed anomaly logs and points to the exact resource responsible.

Setup guide

Set up DoiT 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 DoiT 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 DoiT tools.",
)
response = await agent.run("List recent DoiT data")

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Common questions about DoiT MCP in LlamaIndex

Run pip install llama-index-tools-mcp to get the required library. Instantiate the BasicMCPClient with your Vinkius URL and wrap it in McpToolSpec. Pass the resulting tools to your FunctionAgent.

Yes, the agent calls get_asset_details to fetch configuration metadata. This text is then parsed, embedded, and stored in your vector database. You can then query your infrastructure setup semantically.

The framework query engine uses list_exceeded_cost_budgets to check for overruns. It compares current spending against the limits retrieved by list_cost_budgets. This data grounds the agent's response.

Yes. You can use the allowed_tools filter when initializing the tool spec. This limits the agent to specific actions like reading budgets or listing assets.

All cloud asset configurations and billing metadata are processed locally within your LlamaIndex environment. Vinkius operates a zero-trust architecture for this MCP Server. No cost data is stored or logged on our platform.

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