Pipefy MCP Server for OpenAI Agents SDK 14 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Pipefy through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Pipefy Assistant",
instructions=(
"You help users interact with Pipefy. "
"You have access to 14 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Pipefy"
)
print(result.final_output)
asyncio.run(main())
* 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 Pipefy MCP Server
Connect your Pipefy account to any AI agent and take full control of your process management workflows through natural conversation.
The OpenAI Agents SDK auto-discovers all 14 tools from Pipefy through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries Pipefy, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
What you can do
- Pipe Discovery — List all pipes (processes) in your organization and inspect their structure, phases, and fields
- Card Management — Create, read, update, and delete cards (items/records) flowing through your pipes
- Field Updates — Update specific field values on existing cards as information changes or processes evolve
- Phase Transitions — Move cards between phases to advance workflow steps (e.g., New → In Progress → Done)
- Card Search — Search for cards by field value to find specific items by email, name, ID, or custom data
- Card Cloning — Duplicate existing cards to quickly create similar items with pre-filled field values
- Organization Info — View organization details, members, and available pipes
- User Profile — Check your authenticated user profile and organization memberships
The Pipefy MCP Server exposes 14 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 Pipefy to OpenAI Agents SDK via MCP
Follow these steps to integrate the Pipefy MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 14 tools from Pipefy
Why Use OpenAI Agents SDK with the Pipefy MCP Server
OpenAI Agents SDK provides unique advantages when paired with Pipefy through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Pipefy + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Pipefy MCP Server delivers measurable value.
Automated workflows: build agents that query Pipefy, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries Pipefy, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Pipefy tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Pipefy to resolve tickets, look up records, and update statuses without human intervention
Pipefy MCP Tools for OpenAI Agents SDK (14)
These 14 tools become available when you connect Pipefy to OpenAI Agents SDK via MCP:
clone_card
You must provide the card_id of the card to clone. The new card is created in the same pipe as the original, starting at the first phase. This is useful for creating similar requests, repeating processes, or using an existing card as a template for new items. The cloned card gets a new unique ID but retains all field data. Clone an existing card to create a duplicate
create_card
You must provide the pipe_id and a JSON object containing field values matching the pipe's required fields. Fields are key-value pairs where keys are field IDs and values are the data to store. Optionally specify a phase_id to start the card in a specific phase (defaults to first phase). Example fields: { "name": "John Doe", "email": "john@example.com", "priority": "High" } Create a new card in a Pipefy pipe
delete_card
You must provide the card_id. This action cannot be undone. Use this to remove test cards, duplicates, or items that were created in error. Be careful as this will also remove all associated data including comments, attachments, and field values for that card. Delete a card from a pipe
get_card
Use the card_id obtained from list_cards to inspect full card information. This is useful for reviewing card details before updating fields or moving to another phase. Get detailed information about a specific card
get_organization
Use the organization_id to inspect your organization's structure, understand team membership, and discover available pipes for card management. Get details of a Pipefy organization
get_phase
Phases represent steps in a pipe's workflow. Use the phase_id obtained from get_pipe or list_phases to inspect phase configuration. This helps understand what fields are required at each step of the workflow. Get details of a specific phase
get_pipe
Each pipe represents a workflow or process with multiple phases (steps) and custom fields. Use the pipe_id to get the structure of a pipe before creating cards or managing cards within it. The response includes all phases with their IDs, names, and the custom fields defined for the pipe. Get details of a specific Pipefy pipe (process)
get_user_profile
Use this to verify API token access and discover organization IDs needed for other queries. This is also useful for understanding which organizations and pipes the user has access to. Get the authenticated user profile
list_cards
Cards represent individual items flowing through the pipe's workflow phases (e.g., requests, tasks, tickets, leads). You must provide the pipe_id. Optionally filter by phase_id to see cards in a specific phase. Each card includes title, current phase, completion status, due date, and assignees. Use this to monitor workflow progress and identify cards that need attention. List all cards in a pipe with optional phase filter
list_phases
Each phase represents a stage that cards flow through in the process. Use this to understand the workflow structure and identify phase IDs for filtering cards or moving cards between phases. The response includes phase names and card counts. List all phases in a pipe
list_pipes
Each pipe represents a structured workflow with phases, fields, and cards. You must provide the organization_id which can be found in your Pipefy URL or obtained from get_user_profile. Use this to discover all available pipes before managing cards within them. List all pipes in an organization
move_card_to_phase
You must provide the card_id and the target phase_id. This is the primary way to advance workflow items through the pipe's process steps. Common use cases: moving a request from "New" to "In Review", advancing a lead to "Qualified", or progressing a task to "Completed". The card retains all its field values after moving. Move a card to a different phase in the pipe
search_cards_by_field
This is useful for finding cards by email, name, ID, or any custom field content. You must provide the pipe_id, field_id (the field to search in), and search_value (text to find). Results include card title, current phase, status, and all field values for matching cards. The search uses a "contains" operator for flexible matching. Search cards in a pipe by a specific field value
update_card_field
You must provide the card_id, the field_id of the field to update, and the new value as a string. This is useful for updating card information as requests progress or details change. Common updates: changing priority, updating contact info, modifying descriptions, or setting dates. Update a specific field value on a card
Example Prompts for Pipefy in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Pipefy immediately.
"List all pipes in my organization and show me the cards in the 'IT Support' pipe."
"Create a new purchase request card in the Purchase Requests pipe with these details: Requester: Maria Silva, Item: MacBook Pro 16", Quantity: 2, Justification: Design team replacement."
"Search for all cards in the IT Support pipe where the email field contains 'john@company.com' and show me their current status."
Troubleshooting Pipefy MCP Server with OpenAI Agents SDK
Common issues when connecting Pipefy to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Pipefy + OpenAI Agents SDK FAQ
Common questions about integrating Pipefy MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Pipefy with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Pipefy to OpenAI Agents SDK
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.