How do I find my FlowUs API Token?

Log in to FlowUs, go to [Settings] → [Integrations] → [Bot Integrations], and create a new integration to generate your API Token.

Can I search for specific data within a database?

Yes. Use the `query_database` tool with the database ID. You can optionally provide a JSON filter string to narrow down the results based on your criteria.

What is a 'Block' in FlowUs?

Like Notion, FlowUs uses a block-based structure. Everything from a paragraph of text to an image or a sub-page is considered a block. You can list these using the `list_blocks` tool.

FlowUs MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: FlowUs
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect FlowUs 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 FlowUs "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in FlowUs?"
    )
    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 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.

Pydantic AI validates every FlowUs tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

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 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 FlowUs to Pydantic AI via MCP

Follow these steps to integrate the FlowUs 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 10 tools from FlowUs with type-safe schemas

Why Use Pydantic AI with the FlowUs MCP Server

Pydantic AI provides unique advantages when paired with FlowUs 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 FlowUs integration code

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

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

FlowUs + Pydantic AI Use Cases

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

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

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

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

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

FlowUs MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect FlowUs to Pydantic AI 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 Pydantic AI

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

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

FlowUs + Pydantic AI FAQ

Common questions about integrating FlowUs 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 FlowUs MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.