Can I see which phrases my bot failed to understand?

Yes! Use the `list_training_data` tool. The agent will return a list of unrecognized phrases, helping you identify what needs to be added to your bot's training set.

How do I list all the interactions in a specific story?

Use the `list_story_interactions` tool with the unique story ID. Your agent will fetch all interaction blocks and their configurations.

Where do I find my ChatBot Access Token?

Log in to your ChatBot account and go to the settings section. Look for 'API' or 'Integrations' to generate and copy your Developer Access Token.

ChatBot.com MCP Server for CrewAI 8 tools — connect in under 2 minutes

Name: ChatBot.com
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 8 Tools Framework

Connect your CrewAI agents to ChatBot.com through Vinkius, pass the Edge URL in the `mcps` parameter and every ChatBot.com 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="ChatBot.com Specialist",
    goal="Help users interact with ChatBot.com effectively",
    backstory=(
        "You are an expert at leveraging ChatBot.com 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 ChatBot.com "
        "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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V8 IsolateSandboxed

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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 ChatBot.com MCP Server

Connect your ChatBot.com account to any AI agent and take full control of your conversational automation through natural conversation. Streamline how you build and monitor your customer service bots.

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

Story Oversight — List and retrieve details for all conversational stories and bot workflows natively
Interaction Intelligence — Access and monitor interactions within specific stories to understand user paths flawlessly
User Management — List all users who have interacted with your bot and retrieve their detailed profiles securely
Integration Auditing — List and review configured webhook integrations and entities flawlessly
Training Logistics — Retrieve unrecognized phrases to identify areas where your bot needs additional training flawlessly
System Metadata — Access entity definitions and core account structures directly within your workspace

The ChatBot.com 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 ChatBot.com to CrewAI via MCP

Follow these steps to integrate the ChatBot.com 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 ChatBot.com

Why Use CrewAI with the ChatBot.com MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ChatBot.com 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

ChatBot.com + CrewAI Use Cases

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

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

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

ChatBot.com MCP Tools for CrewAI (8)

These 8 tools become available when you connect ChatBot.com to CrewAI via MCP:

get_chatbot_user_details

Get details for a specific chatbot user

get_story_details

Get detailed information for a specific story

list_chatbot_entities

List custom entities used for NLP matching

list_chatbot_stories

List all stories (bot workflows)

list_chatbot_users

List all users who have interacted with the bot

list_chatbot_webhooks

List all configured webhook integrations

list_story_interactions

List all interactions within a story

list_training_data

List unrecognized phrases that require bot training

Example Prompts for ChatBot.com in CrewAI

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

"List all conversational stories in my account."

"What training data is pending review?"

"Search for users who interacted with the bot today."

Troubleshooting ChatBot.com MCP Server with CrewAI

Common issues when connecting ChatBot.com 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.

ChatBot.com + CrewAI FAQ

Common questions about integrating ChatBot.com 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.