How do I find my QingFlow Access Token?

Log in to QingFlow, go to the Qing Store (轻商城), install the 'OPEN API' plugin under Third-party Connections, and find your Access Token in the plugin configuration.

Can I check why a request is pending approval?

Yes. Using the `get_workflow_status` tool with the application and record IDs, you can retrieve the current approval node, the person responsible, and the status of the process.

How do I format the data for creating a record?

QingFlow uses a specific format for its fields. Use the `get_app_schema` tool first to see the `queId` for each field, then provide the data as a JSON string following the required structure.

QingFlow MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: QingFlow
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 QingFlow. "
            "You have 10 tools available."
        ),
    )

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

asyncio.run(main())

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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 QingFlow MCP Server

Empower your AI agent to orchestrate your business processes with QingFlow, the premier no-code BPM platform for digital transformation. By connecting QingFlow to your agent, you transform complex application management and data orchestration into a natural conversation. Your agent can instantly list your applications, retrieve form schemas, manage records (create, update, delete), and even monitor workflow approval statuses without you ever needing to navigate the technical dashboard. Whether you are managing procurement, HR approvals, or project tracking, your agent acts as a real-time process manager, ensuring your business logic is always executed and optimized.

LlamaIndex agents combine QingFlow tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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

Application Orchestration — List all accessible applications and browse their internal structures.
Data Management — Manage application records with full support for creating, listing, and granular updates.
Workflow Monitoring — Check the current status of automated workflows and approval processes for any record.
Schema Auditing — Retrieve application schemas to understand field structures and widget IDs.
User Coordination — Access workspace user lists to manage assignments and participation effectively.

The QingFlow MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect QingFlow to LlamaIndex via MCP

Follow these steps to integrate the QingFlow MCP Server with LlamaIndex.

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 10 tools from QingFlow

Why Use LlamaIndex with the QingFlow MCP Server

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

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

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

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

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

QingFlow + LlamaIndex Use Cases

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

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

Data enrichment: query QingFlow 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 QingFlow for fresh data

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

QingFlow MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect QingFlow to LlamaIndex via MCP:

create_record

Create a new application record

delete_record

Delete an application record

get_app_schema

Get application field schema

get_record_details

Get record detailed data

get_workflow_status

Get record workflow status

list_apps

List all QingFlow applications

list_data

List records in an application

list_users

List workspace users

list_workflows

List application workflows

update_record

Update an existing record

Example Prompts for QingFlow in LlamaIndex

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

"List all applications in my QingFlow workspace."

"Show me the records for the 'Asset Management' application."

"What is the approval status for record 'req-9920' in 'Leave Request'?"

Troubleshooting QingFlow MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

QingFlow + LlamaIndex FAQ

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