Pydantic AISDK

Activepieces MCP Server

Bring Workflow Automation
to Pydantic AI

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

Learn how to connect Activepieces to Pydantic AI and start using 32 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

GDPR Free for Subscribers

Add PieceApply Flow OperationConfigure Git RepoCreate FlowCreate FolderCreate ProjectCreate Project ReleaseDelete App ConnectionDelete FlowDelete FolderDelete Global ConnectionDelete Project MemberGet FlowGet Flow RunGet Mcp ServerInvite UserList App ConnectionsList Flow RunsList FlowsList FoldersList Global ConnectionsList Project MembersList ProjectsList RecordsList TablesList UsersRotate Mcp TokenUpdate FolderUpdate ProjectUpdate RecordUpsert App ConnectionUpsert Global Connection

Unlock for AI Agents

Ask AI about this MCP Server ChatGPT Claude Perplexity

Compatible with every major AI agent and IDE

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

What is the Activepieces MCP Server?

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

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.

How it works

Subscribe to this server
Enter your Activepieces API Key
Start orchestrating your automations from Claude, Cursor, or any MCP-compatible client

No more manual checking of execution logs or switching tabs to enable/disable flows. Your AI acts as a dedicated automation engineer.

Who is this for?

DevOps & Automation Engineers — monitor flow health and trigger updates directly from the terminal or chat.
Product Operations — manage app connections and verify data consistency across automated workflows.
Marketing Teams — check the status of lead-gen flows and ensure integrations are running smoothly.

Built-in capabilities (32)

add_piece

Add a custom piece to the platform

apply_flow_operation

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

configure_git_repo

Configure Git sync for a project

create_flow

Create a new flow

create_folder

Create a new folder

create_project

Create a new project

create_project_release

Create a project release

delete_app_connection

Delete an app connection

delete_flow

Delete a flow by ID

delete_folder

Delete a folder

delete_global_connection

Delete a global connection

delete_project_member

Remove a member from a project

get_flow

Get a specific flow by ID

get_flow_run

Get detailed execution data for a flow run

get_mcp_server

Get MCP server configuration for AI assistants

invite_user

Invite a user to the platform or project

list_app_connections

List app connections

list_flow_runs

List flow runs

list_flows

List automation flows

list_folders

List folders

list_global_connections

List global connections

list_project_members

List members of a project

list_projects

List projects

list_records

List records in a table

list_tables

List internal data tables

list_users

List users

rotate_mcp_token

Rotate MCP token for a project

update_folder

Update a folder name

update_project

Update project settings

update_record

Update a specific record

upsert_app_connection

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

upsert_global_connection

Create or update a global connection

Why Pydantic AI?

Pydantic AI validates every Activepieces tool response against typed schemas, catching data inconsistencies at build time. Connect 32 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.

—
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 Activepieces integration code
—
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
—
Dependency injection system cleanly separates your Activepieces connection logic from agent behavior for testable, maintainable code

See it in action

Activepieces in Pydantic AI

AI Agent→Vinkius

High Security·Kill Switch·Plug and Play

Why Vinkius

Activepieces and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Activepieces to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready

<40msCold start

60%Token savings

	Raw MCP	Vinkius
Server catalog	Find and host yourself	4,000+ managed
Infrastructure	Self-hosted	Sandboxed V8 isolates
Credential handling	Plaintext in config	Vault + runtime injection
Data loss prevention	None	Configurable DLP policies
Kill switch	None	Global instant shutdown
Financial circuit breakers	None	Per-server limits + alerts
Audit trail	None	Ed25519 signed logs
SIEM log streaming	None	Splunk, Datadog, Webhook
Honeytokens	None	Canary alerts on leak
Custom domains	Not applicable	DNS challenge verified
GDPR compliance	Manual effort	Automated purge + export

Unlock for AI Agents View step-by-step setup guide for Pydantic AI →

Enterprise Security

Why teams choose Vinkius for Activepieces in Pydantic AI

The Activepieces 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. All 32 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Unlock for AI Agents View full Activepieces details →

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

The Vinkius Advantage

How Vinkius secures
Activepieces for Pydantic AI

Every tool call from Pydantic AI to the Activepieces MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start

Ed25519Signed audit chain

60%Token savings

FAQ

Frequently asked questions

Can I check why a specific flow execution failed?

Yes. Use the get_flow_run tool with the Run ID to retrieve detailed execution data, including step results and error messages.

How do I update the status of an existing flow?

You can use the apply_flow_operation tool. It allows you to send an operation payload to change the flow's status or modify its structure.

Can I see which external apps are connected to my project?

Yes, the list_app_connections tool retrieves all credentials and connections configured for a specific Project ID.

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 Activepieces MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.