Flowise MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Flowise 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
Vinkius supports streamable HTTP and SSE.
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 Flowise. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Flowise?"
)
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 Flowise MCP Server
Connect your FlowiseAI instance to any AI agent and take full control of your low-code generative AI application development through natural conversation.
LlamaIndex agents combine Flowise tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Chatflow Orchestration — List and retrieve detailed architectural nodes and edges for all deployed Chatflows within your Flowise instance natively
- Agentic Workflow Control — Access compound Agentflows defining complex AI tasks and multi-step reasoning logic synchronously
- Live AI Prediction — Commands the backend to submit user questions to specific Chatflows and retrieve generated AI responses in real-time
- Execution History Auditing — Pull precise past execution traces and conversational logs to debug logic chains and monitor agent performance limitlessly
- Tool & Integration Discovery — Retrieve custom tools and third-party integrations configured in your Flowise environment to verify available capabilities
- Credential Oversight — Enumerate stored credential components used to authenticate your AI logic chains securely within the platform
- System Health Monitoring — Verify instance status and available base endpoints to ensure your AI orchestration layer is operational
The Flowise MCP Server exposes 7 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 Flowise to LlamaIndex via MCP
Follow these steps to integrate the Flowise 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 7 tools from Flowise
Why Use LlamaIndex with the Flowise MCP Server
LlamaIndex provides unique advantages when paired with Flowise through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Flowise tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Flowise tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Flowise, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Flowise tools were called, what data was returned, and how it influenced the final answer
Flowise + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Flowise MCP Server delivers measurable value.
Hybrid search: combine Flowise real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Flowise 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 Flowise for fresh data
Analytical workflows: chain Flowise queries with LlamaIndex's data connectors to build multi-source analytical reports
Flowise MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Flowise to LlamaIndex via MCP:
get_chatflow
Get chatflow details
get_history
Get chat execution history
list_agentflows
List agentflows
list_chatflows
List chatflows
list_credentials
List credentials
list_tools
List available tools
predict
Run prediction on chatflow
Example Prompts for Flowise in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Flowise immediately.
"Ask chatflow 'abc-123': 'Summarize this document: [Context]'"
"List all active chatflows in my instance"
"Show me the execution history for chatflow 'Legal-Assistant'"
Troubleshooting Flowise MCP Server with LlamaIndex
Common issues when connecting Flowise to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlowise + LlamaIndex FAQ
Common questions about integrating Flowise MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Flowise with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Flowise to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.