How to Use the HiFlow MCP in LlamaIndex

Q: How does LlamaIndex use HiFlow data to answer questions about project profitability?

LlamaIndex indexes the output of getjob, listinvoices, and listtimesheets for a project. When you ask about profitability, it queries this index to find the total invoiced amount and the total hours logged, then calculates the result based on that grounded data.

Turn your live HiFlow data into a queryable knowledge base using LlamaIndex RAG pipelines.

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Connect HiFlow MCP to LlamaIndex

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Setup HiFlow with LlamaIndex

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Index Your Business in Real Time

LlamaIndex doesn't just use tool output once. It takes the data from `list_invoices` and `list_customers` and adds it to a vector index. Your live operational data becomes a searchable knowledge base your agent can query. This lets you ask plain English questions about your business. Instead of pulling a manual report, you just ask your agent, 'Which projects from last month are still awaiting payment?' It already knows the answer because it indexed the output from `list_jobs` and `list_invoices`.

Get Answers Grounded in Your Data

A standard LLM might guess about your business. A LlamaIndex agent gives you facts. When you ask about a project, it pulls the answer straight from its indexed knowledge of `get_job` and `get_activity` calls. It proves its work. You get the answer, and you get the citations—the specific HiFlow MCP tool outputs that informed it. You can actually trust the answers you're getting from your AI agent.

Query Your Business History

You aren't just querying what's happening right now. Because LlamaIndex builds a knowledge base over time, you can analyze trends and look back at previous states of your business. Ask questions like, 'Compare this month's new estimates to last month's.' Your agent can query its index, which contains historical data from `list_estimates`, and give you a clear, data-backed comparison. It turns raw tool output into business intelligence.

Setup guide

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

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Common questions about HiFlow MCP in LlamaIndex

LlamaIndex indexes the output of `get_job`, `list_invoices`, and `list_timesheets` for a project. When you ask about profitability, it queries this index to find the total invoiced amount and the total hours logged, then calculates the result based on that grounded data.

You decide. You can configure your LlamaIndex application to update the index on a schedule, on-demand, or every time a tool is called. For HiFlow data, a periodic update is usually the most efficient approach.

Absolutely. You can build a RAG pipeline where your team can ask the chatbot questions like 'What's the status of the Acme project?' and get an answer sourced directly from the indexed output of the `get_job` tool.

The MCP Server abstracts the raw API. If HiFlow changes, Vinkius updates the server. Your LlamaIndex tool specification will refresh automatically, and your agent will see the new tool schemas without you needing to change your code.

You control where the index is stored. The HiFlow data—customer records, job details, invoice figures—is pulled by your agent and placed into a vector store that you manage. The data doesn't live on our servers; it lives in your infrastructure.

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