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 Pydantic AI 12 tools — connect in under 2 minutes

Name: Flow
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Flow through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Flow "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Flow?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

Pydantic AI validates every Flow tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Flow MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Flow MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Flow integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Flow connection logic from agent behavior for testable, maintainable code

Flow + Pydantic AI Use Cases

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

Type-safe data pipelines: query Flow with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Flow tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Flow and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Flow responses and write comprehensive agent tests

Flow MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Flow to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Flow + Pydantic AI FAQ

Common questions about integrating Flow MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Flow MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.