Activepieces MCP Server for CrewAIGive CrewAI instant access to 32 tools to Add Piece, Apply Flow Operation, Configure Git Repo, and more

Name: Activepieces
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

Ask AI about this MCP Server for CrewAI

Open in ChatGPT Open in Claude Open in Perplexity

The Activepieces MCP Server for CrewAI is a standout in the Productivity category — giving your AI agent 32 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Activepieces Specialist",
    goal="Help users interact with Activepieces effectively",
    backstory=(
        "You are an expert at leveraging Activepieces 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 Activepieces "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 32 available tools "
        "and what they can do."
    ),
)

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

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Activepieces MCP Server

Connect your Activepieces account to any AI agent to orchestrate complex automations and monitor your business workflows through natural language.

When paired with CrewAI, Activepieces becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Activepieces 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

Flow Management — List, create, retrieve, and delete automation flows within your projects using list_flows and create_flow.
Execution Monitoring — Track flow runs, check statuses, and inspect detailed step results for debugging with list_flow_runs and get_flow_run.
App Connections — Manage credentials and connections for external services like Slack, Discord, or Google Sheets via list_app_connections.
Flow Operations — Apply structural changes or status updates to existing flows programmatically using apply_flow_operation.
Organization — List and manage folders to keep your automation workspace tidy with list_folders.

The Activepieces MCP Server exposes 32 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 32 Activepieces tools available for CrewAI

When CrewAI connects to Activepieces through Vinkius, your AI agent gets direct access to every tool listed below — spanning workflow-automation, no-code, business-process, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

add

Add piece on Activepieces

Add a custom piece to the platform

apply

Apply flow operation on Activepieces

g., MOVE_ACTION, CHANGE_STATUS). Apply an operation to a flow

configure

Configure git repo on Activepieces

Configure Git sync for a project

create

Create flow on Activepieces

Create a new flow

create

Create folder on Activepieces

Create a new folder

create

Create project on Activepieces

Create a new project

create

Create project release on Activepieces

Create a project release

delete

Delete app connection on Activepieces

Delete an app connection

delete

Delete flow on Activepieces

Delete a flow by ID

delete

Delete folder on Activepieces

Delete a folder

delete

Delete global connection on Activepieces

Delete a global connection

delete

Delete project member on Activepieces

Remove a member from a project

get

Get flow on Activepieces

Get a specific flow by ID

get

Get flow run on Activepieces

Get detailed execution data for a flow run

get

Get mcp server on Activepieces

Get MCP server configuration for AI assistants

invite

Invite user on Activepieces

Invite a user to the platform or project

list

List app connections on Activepieces

List app connections

list

List flow runs on Activepieces

List flow runs

list

List flows on Activepieces

List automation flows

list

List folders on Activepieces

List folders

list

List global connections on Activepieces

List global connections

list

List project members on Activepieces

List members of a project

list

List projects on Activepieces

List projects

list

List records on Activepieces

List records in a table

list

List tables on Activepieces

List internal data tables

list

List users on Activepieces

List users

rotate

Rotate mcp token on Activepieces

Rotate MCP token for a project

update

Update folder on Activepieces

Update a folder name

update

Update project on Activepieces

Update project settings

update

Update record on Activepieces

Update a specific record

upsert

Upsert app connection on Activepieces

Supports SECRET_TEXT, OAUTH2, BASIC_AUTH, CUSTOM_AUTH, etc. Create or update an app connection

upsert

Upsert global connection on Activepieces

Create or update a global connection

Connect Activepieces to CrewAI via MCP

Follow these steps to wire Activepieces into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 32 tools from Activepieces

Why Use CrewAI with the Activepieces MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Activepieces 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

Activepieces + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Activepieces 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 Activepieces, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Activepieces 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 Activepieces against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Activepieces in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Activepieces immediately.

"List all active automation flows in project 'proj_123'."

"Show me the last 5 runs for flow ID 'flow_1'."

"Create a new flow named 'Customer Support Sync' in project 'proj_123'."

Troubleshooting Activepieces MCP Server with CrewAI

Common issues when connecting Activepieces to CrewAI through 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.

Activepieces + CrewAI FAQ

Common questions about integrating Activepieces 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.