FlowUs MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FlowUs as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 FlowUs. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in FlowUs?"
)
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 FlowUs MCP Server
Empower your AI agent to orchestrate your knowledge base with FlowUs, the versatile collaboration platform for modern individuals and teams. By connecting FlowUs to your agent, you transform complex page organization and database management into a natural conversation. Your agent can instantly list your pages, retrieve block-level content, manage multi-dimensional databases, and even create new entries without you needing to navigate the complex web interface. Whether you are managing personal notes, project documentation, or shared team databases, your agent acts as a real-time knowledge assistant, keeping your workspace organized and your information accessible.
LlamaIndex agents combine FlowUs tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Page Orchestration — List all accessible pages and retrieve detailed metadata about your workspace structure.
- Block Management — Browse content blocks within pages to access text and media information instantly.
- Database Control — Manage multi-dimensional tables (databases) with full support for querying and creating new rows.
- Workspace Organization — Create and update pages to maintain a clean and structured knowledge base.
- Team Coordination — Access workspace user lists to manage participation and collaboration effectively.
The FlowUs 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 FlowUs to LlamaIndex via MCP
Follow these steps to integrate the FlowUs 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 FlowUs
Why Use LlamaIndex with the FlowUs MCP Server
LlamaIndex provides unique advantages when paired with FlowUs through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine FlowUs tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain FlowUs tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query FlowUs, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what FlowUs tools were called, what data was returned, and how it influenced the final answer
FlowUs + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the FlowUs MCP Server delivers measurable value.
Hybrid search: combine FlowUs real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query FlowUs 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 FlowUs for fresh data
Analytical workflows: chain FlowUs queries with LlamaIndex's data connectors to build multi-source analytical reports
FlowUs MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect FlowUs to LlamaIndex via MCP:
create_database_row
Add row to database
create_page
Create a new FlowUs page
get_database
Get database schema
get_page
Get page details
list_blocks
) within a specific page. List page blocks
list_databases
List all FlowUs databases
list_pages
List all FlowUs pages
list_users
List workspace users
query_database
Query database rows
update_page
Update an existing page
Example Prompts for FlowUs in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with FlowUs immediately.
"List all pages in my FlowUs workspace."
"Query the 'Product Backlog' database for items with 'High' priority."
"Add a new row to the 'User Feedback' database with Name='Renato' and Feedback='Love the AI integration!'."
Troubleshooting FlowUs MCP Server with LlamaIndex
Common issues when connecting FlowUs to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFlowUs + LlamaIndex FAQ
Common questions about integrating FlowUs 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 FlowUs 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 FlowUs to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.