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 CrewAI 10 tools — connect in under 2 minutes

Name: FlowUs
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to FlowUs through Vinkius, pass the Edge URL in the `mcps` parameter and every FlowUs tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="FlowUs Specialist",
    goal="Help users interact with FlowUs effectively",
    backstory=(
        "You are an expert at leveraging FlowUs tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in FlowUs "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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 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.

When paired with CrewAI, FlowUs becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call FlowUs tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the FlowUs MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from FlowUs

Why Use CrewAI with the FlowUs MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with FlowUs through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

FlowUs + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries FlowUs for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries FlowUs, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain FlowUs tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries FlowUs against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

FlowUs MCP Tools for CrewAI (10)

These 10 tools become available when you connect FlowUs to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

FlowUs + CrewAI FAQ

Common questions about integrating FlowUs MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.