How do I find the Workspace ID for my Flow account?

You can use the 'list_workspaces' tool. The agent will retrieve all workspaces you have access to and return their names and unique IDs, which you can then use for other operations.

Can I mark a task as completed using the agent?

Yes! Use the 'update_task' tool, provide the Task ID, and set the 'status' parameter to 'completed'. The task will be immediately updated in Flow.

How do I see the discussion history for a task?

Use the 'list_task_comments' tool and provide the Task ID. The agent will retrieve all messages and replies associated with that task, allowing you to catch up on the discussion.

Flow MCP Server for LlamaIndex 12 tools — connect in under 2 minutes

Name: Flow
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 12 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Flow 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 Flow. "
            "You have 12 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Flow?"
    )
    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 Flow MCP Server

Connect your Flow account to any AI agent and automate your project management and team collaboration through the Model Context Protocol (MCP). Flow (getflow.com) provides a clean and powerful platform for organizing work, tracking task progress, and facilitating team discussions. Now, you can manage your workspaces, projects, and individual tasks directly through natural conversation.

LlamaIndex agents combine Flow tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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

Project Coordination — List all projects within your workspaces and retrieve detailed metadata, including ownership and due dates.
Task Management — Create, update, and list tasks across workspaces, projects, or specific task lists. Change statuses (incomplete/completed) instantly.
Organized Lists — Access and list task groups (Lists) within projects to maintain a clear hierarchy of work.
Team Interaction — List all workspace members and teams, and participate in task discussions by reading or adding comments.
Workspace Oversight — Get a high-level view of all the top-level workspaces you belong to.
Real-time Updates — Fetch specific task details or metadata to keep your team informed and your projects on track.

The Flow 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.

How to Connect Flow to LlamaIndex via MCP

Follow these steps to integrate the Flow 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 12 tools from Flow

Why Use LlamaIndex with the Flow MCP Server

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

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

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

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

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

Flow + LlamaIndex Use Cases

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

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

Data enrichment: query Flow 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 Flow for fresh data

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

Flow MCP Tools for LlamaIndex (12)

These 12 tools become available when you connect Flow to LlamaIndex via MCP:

add_task_comment

create_task

Create a new task

get_project

Get project details

get_task

Get task details

list_projects

List projects in workspace

list_task_comments

List task discussions

list_task_lists

List lists in project

list_tasks

List tasks

list_workspace_members

List team members

list_workspace_teams

List workspace teams

list_workspaces

List top-level workspaces

update_task

). Update an existing task

Example Prompts for Flow in LlamaIndex

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

"List all my Flow projects in the 'Marketing' workspace."

"Create a new task: 'Review final design mockup' in the 'Design' list."

"Add a comment to task 'task_123': 'Design looks great, proceed to coding'."

Troubleshooting Flow MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Flow + LlamaIndex FAQ

Common questions about integrating Flow 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.