FlowiseAI MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Execute Chatflow Prediction, Get Chatflow Details, Get Server Version, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FlowiseAI 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 App Connector for LlamaIndex
The FlowiseAI app connector for LlamaIndex is a standout in the Friends Mcp category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 FlowiseAI. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in FlowiseAI?"
)
print(response)
asyncio.run(main())
* 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 FlowiseAI MCP Server
Connect your FlowiseAI (self-hosted) instance to any AI agent and take full control of your LLM orchestration and RAG workflows through natural conversation.
LlamaIndex agents combine FlowiseAI 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.
What you can do
- Prediction Orchestration — Trigger specific chatflows and retrieve LLM-generated responses programmatically using natural language inputs
- Chatflow Management — List all orchestration flows and retrieve detailed technical structures and metadata to monitor your AI agents
- Vector Intelligence — Programmatically upsert documents or raw data into the vector stores linked to your chatflows to ensure high-fidelity context
- Component Oversight — Access server-wide credentials, custom tools, and global variables to manage your complete Flowise ecosystem
- Operational Visibility — Monitor user feedback, leads, and assistant profiles directly through your agent for instant reporting
The FlowiseAI MCP Server exposes 12 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.
All 12 FlowiseAI tools available for LlamaIndex
When LlamaIndex connects to FlowiseAI through Vinkius, your AI agent gets direct access to every tool listed below — spanning llm-workflows, rag-pipelines, chatbot-development, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Trigger an LLM flow prediction
Get details for a specific chatflow
Get Flowise server version
List OpenAI-style assistants
List user feedback for a chatflow
List all LLM orchestration flows
List custom tools
List captured leads
List global variables
List configured credentials
List chatflow templates
Push data into a vector store
Connect FlowiseAI to LlamaIndex via MCP
Follow these steps to wire FlowiseAI into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the FlowiseAI MCP Server
LlamaIndex provides unique advantages when paired with FlowiseAI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FlowiseAI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FlowiseAI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FlowiseAI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FlowiseAI tools were called, what data was returned, and how it influenced the final answer
FlowiseAI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FlowiseAI MCP Server delivers measurable value.
Hybrid search: combine FlowiseAI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FlowiseAI 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 FlowiseAI for fresh data
Analytical workflows: chain FlowiseAI queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for FlowiseAI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FlowiseAI immediately.
"List all my chatflows in Flowise."
"Execute chatflow 'cf_1' with question: 'How do I reset my password?'"
"Upsert this data into vector store for chatflow 'cf_2': [data]"
Troubleshooting FlowiseAI MCP Server with LlamaIndex
Common issues when connecting FlowiseAI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlowiseAI + LlamaIndex FAQ
Common questions about integrating FlowiseAI MCP Server with LlamaIndex.