How to Use the FlowUs MCP in LlamaIndex

Q: Can LlamaIndex query FlowUs databases?

It can. By using the querydatabase tool, the agent retrieves rows that match your semantic search terms. These results are then used to answer your questions.

Index your FlowUs workspace into LlamaIndex for grounded, accurate AI answers.

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Connect FlowUs MCP to LlamaIndex

Create your Vinkius account to connect FlowUs to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup FlowUs with LlamaIndex

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Index your workspace content

Turn your messy notes into a searchable vector store. By calling `list_pages` and `list_blocks`, you pull the raw content into your knowledge base so LlamaIndex can index it for semantic retrieval. This approach prevents the agent from making things up. It grounds every response in the actual text found inside your workspace pages.

Query live database rows

Use `query_database` to bring live records into your retrieval pipeline. Instead of static files, you get dynamic data that changes whenever your team updates a row in your workspace. LlamaIndex treats these rows as searchable objects. This makes it easy to find specific project status updates or task lists without leaving your development environment.

Programmatic page management

Your agent can create new documentation pages using `create_page` whenever it finds a gap in your existing knowledge base. It writes the results of your RAG queries directly back into your workspace. This keeps your documentation living and breathing. The tools `get_page` and `update_page` ensure that the AI is always working with the latest version of your records.

Setup guide

Set up FlowUs MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all FlowUs MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to FlowUs tools.",
)
response = await agent.run("List recent FlowUs data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FlowUs. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about FlowUs MCP in LlamaIndex

The server provides an MCP interface that LlamaIndex consumes as a tool spec. It pulls the pages and databases you specify into its vector index.

It can. By using the `query_database` tool, the agent retrieves rows that match your semantic search terms. These results are then used to answer your questions.

Yes. You wrap the tools in a client spec and pass them to the agent. This gives the agent the ability to read and index your workspace data.

Data privacy is maintained by the Vinkius sandbox. Only the text content from pages and database rows you access is passed to your agent's context window.

You trigger the agent to re-run the tool calls periodically. This refreshes the vector index with the latest content retrieved via `list_pages`.

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