Can I train my bot on a new PDF file through the agent?

You can trigger the training process using the `trigger_bot_training` tool once you have added files via the ChatFly dashboard. Future updates will support direct file uploads.

How do I see the history of a specific conversation?

Use the `get_conversation_history` tool with the unique conversation ID. Your agent will fetch the full transcript of messages between the user and the bot.

Where do I find my ChatFly API Key?

Log in to your ChatFly dashboard and navigate to the 'API' or 'Integrations' section. You can generate and copy your API key from there.

CHATFLY MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: CHATFLY
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to CHATFLY through the 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.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 8 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 CHATFLY MCP Server

Connect your CHATFLY account to any AI agent and take full control of your custom chatbot workflows through natural conversation. Train and monitor your own AI agents using your business data.

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 the Vinkius with zero configuration overhead.

What you can do

Chatbot Oversight — List and retrieve details for all custom AI chatbots in your account natively
Knowledge Logistics — List all uploaded documents and data sources used for bot training flawlessly
Training Automation — Trigger the training process for your chatbots to ingest new data securely
Conversation Intelligence — Access recent chat conversations and full message history flawlessly
Live Messaging — Send messages to your chatbots and receive AI-generated responses in real-time
System Monitoring — Retrieve core account information and monitor your AI usage quotas directly within your workspace

The CHATFLY MCP Server exposes 8 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 CHATFLY to CrewAI via MCP

Follow these steps to integrate the CHATFLY 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 8 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 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

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

CHATFLY MCP Tools for CrewAI (8)

These 8 tools become available when you connect CHATFLY to CrewAI via MCP:

get_chatbot_details

Get detailed information for a specific chatbot

get_chatfly_account_info

Retrieve core account and quota information

get_conversation_history

Retrieve the message history for a specific conversation

list_chatfly_bots

List all AI chatbots in your account

list_fly_conversations

List recent chat conversations

list_uploaded_documents

List all files uploaded to the knowledge base

send_bot_message

Send a message to a chatbot and receive a response

trigger_bot_training

Trigger the training process for a chatbot

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 active chatbots in CHATFLY."

"Show me the last 5 conversations for bot 'Support Assistant'."

"Send a test message to bot ID 123: 'How do I reset my password?'"

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

The 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.