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HiFlow MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Customer, Get Activity, Get Customer, and more

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

Ask AI about this MCP Server for LlamaIndex

The HiFlow MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 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 HiFlow. "
            "You have 12 tools available."
        ),
    )

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

asyncio.run(main())
HiFlow
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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 HiFlow MCP Server

The HiFlow MCP server bridges your AI agent with your enterprise workflows. Trigger processes, approve pending steps, and query real-time process states through a seamless conversational interface.

LlamaIndex agents combine HiFlow tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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.

The HiFlow MCP Server exposes 12 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 12 HiFlow tools available for LlamaIndex

When LlamaIndex connects to HiFlow through Vinkius, your AI agent gets direct access to every tool listed below — spanning workflow-automation, bpm, process-management, 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 customer on HiFlow

Create a new customer

get

Get activity on HiFlow

Retrieve activity dashboard information

get

Get customer on HiFlow

Retrieve details for a specific customer

get

Get estimate on HiFlow

Retrieve details for a specific estimate

get

Get invoice on HiFlow

Retrieve details for a specific invoice

get

Get job on HiFlow

Retrieve details for a specific job

get

Get me on HiFlow

Retrieve information about the current user

list

List customers on HiFlow

List all customers

list

List estimates on HiFlow

List all estimates (quotes)

list

List invoices on HiFlow

List all invoices

list

List jobs on HiFlow

List all jobs/projects

list

List timesheets on HiFlow

List all timesheet entries

Connect HiFlow to LlamaIndex via MCP

Follow these steps to wire HiFlow into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

01

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save to agent.py and run: python agent.py
04

Explore tools

The agent discovers 12 tools from HiFlow

Why Use LlamaIndex with the HiFlow MCP Server

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

01

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

02

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

03

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

04

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

HiFlow + LlamaIndex Use Cases

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

01

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

02

Data enrichment: query HiFlow to augment indexed data with live information before generating user-facing responses

03

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying HiFlow for fresh data

04

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

Example Prompts for HiFlow in LlamaIndex

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

01

"List my pending approvals."

02

"Start the 'Employee Onboarding' process for John Doe."

03

"Approve task ID 9481."

Troubleshooting HiFlow MCP Server with LlamaIndex

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

01

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

HiFlow + LlamaIndex FAQ

Common questions about integrating HiFlow MCP Server with LlamaIndex.

01

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.
02

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query HiFlow tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
03

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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