How do I find my ChatFly API Key?

Log in to your account, navigate to **Account Settings** > **API Keys**, and generate a new key for your integration.

Can I train a bot on a specific URL?

Yes! Use the `upload_data_source` tool and provide the bot ID along with the website URL you want the bot to learn from.

How do I test a bot's response via AI?

Use the `chat` tool to send a message to a specific bot ID. Your agent will return the high-fidelity AI response generated by ChatFly.

INSTANT PLUG AND PLAY

ChatFly MCP Server for CrewAIGive CrewAI instant access to 7 tools to Chat, Create Bot, Get Bot, and more

Name: ChatFly
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 7 Tools Framework

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

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

Open in ChatGPT Open in Claude Open in Perplexity

The ChatFly app connector for CrewAI is a standout in the Customer Support category — giving your AI agent 7 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ChatFly Specialist",
    goal="Help users interact with ChatFly effectively",
    backstory=(
        "You are an expert at leveraging ChatFly 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 ChatFly "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 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 ChatFly MCP Server

Connect your ChatFly account to any AI agent and take full control of your custom chatbot orchestration and automated knowledge ingestion workflows through natural conversation.

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

Bot Orchestration — Create and manage multiple high-fidelity AI chatbot instances programmatically, including configuring welcome messages and internal metadata
Knowledge Ingestion — Programmatically train your bots by uploading website URLs and documents to coordinate an accurate, data-driven knowledge base
Real-Time Interaction — Send messages and retrieve AI responses from specific bots to test performance or integrate chat into custom business applications
Source Management — Access and monitor your complete directory of data sources (URLs, docs) to oversee the information feeding your digital assistants
Operational Monitoring — Track chatbot performance, session histories, and account-level status directly through your agent for instant reporting

The ChatFly MCP Server exposes 7 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.

All 7 ChatFly tools available for CrewAI

When CrewAI connects to ChatFly through Vinkius, your AI agent gets direct access to every tool listed below — spanning chatbot-builder, conversational-ai, lead-qualification, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

chat

Interact with a chatbot

create_bot

Provide name and welcome message. Create a new chatbot

get_bot

Get details of a specific bot

list_bots

List all chatbots

list_data_sources

List data sources for a bot

update_bot

Update an existing bot

upload_data_source

Add a knowledge source to a bot

Connect ChatFly to CrewAI via MCP

Follow these steps to wire ChatFly into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the 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 7 tools from ChatFly

Why Use CrewAI with the ChatFly MCP Server

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

ChatFly + CrewAI Use Cases

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

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

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

Example Prompts for ChatFly in CrewAI

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

"List all my available chatbots in ChatFly."

"Train 'bot_1' by ingesting 'https://vinkius.com/faq'."

"Ask 'bot_1': 'What are your support hours?'."

Troubleshooting ChatFly MCP Server with CrewAI

Common issues when connecting ChatFly 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.

ChatFly + CrewAI FAQ

Common questions about integrating ChatFly 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.