How do I obtain my Feathery API Key?

Log in to your Feathery dashboard, navigate to **Settings > API**, and generate a new key. Ensure you copy the token immediately as it is only shown once.

Can I see the data submitted by a specific user?

Yes! Use the `get_user_data` tool with the specific User ID to retrieve all field values submitted by that user across any of your forms.

How can I troubleshoot integration errors via chat?

The `list_connector_logs` tool retrieves recent error logs for your API connectors. This allows your agent to identify exactly which field mapping or endpoint caused a failure.

Feathery MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Feathery
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Feathery through Vinkius, pass the Edge URL in the `mcps` parameter and every Feathery 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="Feathery Specialist",
    goal="Help users interact with Feathery effectively",
    backstory=(
        "You are an expert at leveraging Feathery 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 Feathery "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 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 Feathery MCP Server

Connect your Feathery.io account to any AI agent and take full control of your form automation and user data management through natural conversation.

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

User Orchestration — List all users in your environment and fetch detailed profiles including submission history natively
Submission Intelligence — Retrieve granular field data submitted by specific users across all your automated forms flawlessly
Session Monitoring — Query current form sessions to understand user progress and friction points in real-time
Connector Auditing — List API connector logs to verify data synchronization and troubleshoot integration errors synchronously
Form Management — List all active forms and retrieve structural details and metadata directly from the cloud
Workflow Tracking — Inspect automated workflows and their execution status to ensure seamless user journeys
Identity Context — Verify your API token user profile and account information through the agent flawlessly

The Feathery MCP Server exposes 11 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 Feathery to CrewAI via MCP

Follow these steps to integrate the Feathery 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 11 tools from Feathery

Why Use CrewAI with the Feathery MCP Server

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

Feathery + CrewAI Use Cases

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

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

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

Feathery MCP Tools for CrewAI (11)

These 11 tools become available when you connect Feathery to CrewAI via MCP:

get_account_info

Get Feathery account details

get_form_details

Get details for a specific form

get_form_session

Retrieve the current state/session of a specific form for a user

get_me

Get current API token identity info

get_user_data

Get all field values submitted by a specific user across forms

get_workflow_details

Get details for a specific workflow

list_connector_logs

List recent API connector error logs for a specific form

list_environments

List available Feathery environments

list_forms

List all forms in your Feathery account

list_users

List all users in your Feathery environment

list_workflows

List all automated workflows

Example Prompts for Feathery in CrewAI

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

"List all active forms in my account."

"Show me the data submitted by user user_99."

"Check if there are any connector errors for the Onboarding form."

Troubleshooting Feathery MCP Server with CrewAI

Common issues when connecting Feathery 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.

Feathery + CrewAI FAQ

Common questions about integrating Feathery 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.