How do I get a Pipefy API token and where do I find it?

Log in to Pipefy, click on your **Profile icon** (top-right), go to **Profile > API Access**, and generate a new token. Alternatively, use **Service Accounts** for team-based access. Copy the token immediately — it starts with `eyJhbG...`. Paste it into the API token field below. This token authenticates all GraphQL API requests to `https://api.pipefy.com/graphql`.

How do I move a card from one phase to another in a pipe?

Use the `move_card_to_phase` tool with the card_id (from list_cards or get_card) and the target phase_id (from get_pipe or list_phases). This advances the card through your workflow. For example, moving a card from 'New Requests' to 'In Review' phase. The card retains all its field values after moving. This is the primary way to progress items through your process.

Can I search for cards by a specific field value like email or customer name?

Yes! Use the `search_cards_by_field` tool with the pipe_id, field_id (the specific field to search in), and search_value (the text to find). This searches all cards in the pipe where that field contains your search text. It's perfect for finding cards by customer email, name, order number, or any custom field. Results include full card details with all field values.

How do I create a new card with custom field values?

Use the `create_card` tool with the pipe_id and a JSON object containing field values. The fields parameter should be a JSON object where keys are field IDs (from get_pipe) and values are the data to store. Example: `{ "name": "John Doe", "email": "john@example.com", "priority": "High" }`. You can also optionally specify a phase_id to start the card in a specific phase instead of the default first phase.

Pipefy MCP Server for CrewAI 14 tools — connect in under 2 minutes

Name: Pipefy
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 14 Tools Framework

Connect your CrewAI agents to Pipefy through the Vinkius — pass the Edge URL in the `mcps` parameter and every Pipefy 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="Pipefy Specialist",
    goal="Help users interact with Pipefy effectively",
    backstory=(
        "You are an expert at leveraging Pipefy 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 Pipefy "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 14 available tools "
        "and what they can do."
    ),
)

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

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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 Pipefy MCP Server

Connect your Pipefy account to any AI agent and take full control of your process management workflows through natural conversation.

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

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 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 Pipefy to CrewAI via MCP

Follow these steps to integrate the Pipefy 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 14 tools from Pipefy

Why Use CrewAI with the Pipefy MCP Server

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

Pipefy + CrewAI Use Cases

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

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

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

Pipefy MCP Tools for CrewAI (14)

These 14 tools become available when you connect Pipefy to CrewAI 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 CrewAI

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

Common issues when connecting Pipefy 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

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

Pipefy + CrewAI FAQ

Common questions about integrating Pipefy 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.