ChatBot.com MCP. Audit bot performance and user data via chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
ChatBot.com connects your AI agent to your bot workflows. Use it to list stories, check user profiles, and review training data directly from your chat interface.
It lets you manage conversational AI performance and audit user interactions without opening the main dashboard. You get full oversight of your bot's performance and user data.
What your AI agents can do
Get chatbot user details
Retrieves specific profile details for a given user who interacted with the bot.
Get story details
Fetches detailed information about a single, specific bot workflow (story).
List chatbot entities
Lists all custom entities used by the bot for matching natural language phrases.
List all available bot stories and retrieve detailed information about any specific workflow.
List all users who've talked to the bot and review every interaction they had within a specific story.
Pull a list of unrecognized user phrases, showing exactly what the bot needs to be trained on next.
Get detailed profiles for individual users who interacted with the bot.
List all webhooks and custom entities to check the bot's technical configuration.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
ChatBot.com MCP Server: 8 Tools for Bot & Story Management
These tools let you list, retrieve, and audit every piece of data related to your bot's performance, from user profiles to training phrases.
019d756dget chatbot user details
Retrieves specific profile details for a given user who interacted with the bot.
019d756dget story details
Fetches detailed information about a single, specific bot workflow (story).
019d756dlist chatbot entities
Lists all custom entities used by the bot for matching natural language phrases.
019d756dlist chatbot stories
Lists every active bot workflow or 'story' configured in the account.
019d756dlist chatbot users
Provides a list of every user who has ever interacted with the bot.
019d756dlist chatbot webhooks
Lists all webhook integrations currently set up for the bot.
019d756dlist story interactions
Retrieves a list of all individual messages and turns within a specific bot story.
019d756dlist training data
Lists phrases that the bot didn't recognize, which you need to add to its training data.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with ChatBot.com, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
ChatBot.com lets your AI agent talk directly to your bot workflows. You can list all active bot stories and get specific details about any workflow using list_chatbot_stories and get_story_details. You can check every user who's talked to the bot by calling list_chatbot_users, and then pull up specific profile info for any single user with get_chatbot_user_details.
You'll see every message and turn a user had inside a specific bot story by running list_story_interactions. Your AI client can also look at all the custom entities the bot uses for natural language matching by running list_chatbot_entities, and it can list all the webhook integrations set up for the bot using list_chatbot_webhooks.
You can review what the bot doesn't understand by pulling a list of unrecognized phrases using list_training_data.
How ChatBot.com MCP Works
- 1 First, connect your AI agent to the ChatBot.com server using your Developer Access Token.
- 2 Then, prompt your agent with a specific request, like 'List all stories' or 'Show me the interactions for user X'.
- 3 Your agent runs the necessary tool calls and returns structured data—user lists, story details, or training data—right in your chat window.
The bottom line is, you get real-time access to complex bot data and user metrics without leaving your chat interface.
Who Is ChatBot.com MCP For?
The Customer Experience Manager who needs to prove ROI on a bot's performance. The Conversational Designer who wants to audit user flows before launch. The Support Team lead who needs fast access to chat history. If you spend too much time clicking through internal dashboards, this is for you.
Reviews bot performance metrics and analyzes user interactions using natural language prompts.
Audits story flow and interaction paths across multiple users without opening the complex internal dashboard.
Quickly looks up user details and full chat histories straight from their daily chat interface.
What Changes When You Connect
- See a user's full history immediately. Instead of opening a case management system, use
get_chatbot_user_detailsto pull a user's profile directly into your chat window. - Map out user journeys easily. You don't need to guess how users move through the bot.
list_story_interactionsshows every step and message within a story. - Fix bot blind spots fast. If the bot fails on certain phrases, use
list_training_datato get a list of unrecognized phrases and update the model. - Audit the whole setup. Need to know what integrations are active? Run
list_chatbot_webhooksto see every connected system and entity definition. - Keep track of all users. Instead of relying on tribal knowledge,
list_chatbot_usersgives you a complete roster of every person who has used the bot. - Understand the bot's structure. Use
list_chatbot_storiesto see every workflow available, making it easy to know what data is even trackable.
Real-World Use Cases
Diagnosing a Broken Bot Flow
A support agent notices a user is stuck in the bot loop. Instead of manually checking logs across three different tabs, the agent asks the AI to run list_story_interactions for that user and story. This instantly shows the exact sequence of messages and the point where the bot failed, allowing them to fix the flow immediately.
Onboarding a New Product Line
The Product Manager wants to ensure the bot can handle product X questions. They use list_chatbot_entities to see what data points the bot is currently looking for (like product codes or model numbers) and then check list_chatbot_stories to see if a new story flow needs to be built.
Gathering Data for Model Retraining
The Data Scientist suspects the bot is missing common cancellation phrases. They run list_training_data to pull a massive list of unrecognized phrases like 'cancel my subscription' and 'speak to a human', which they then use to retrain the core model.
Compliance Audit of User Data
The CX Manager needs to verify who used the bot last quarter. They use list_chatbot_users to get a list of all unique user IDs, and then run get_chatbot_user_details on key accounts to confirm data retention compliance.
The Tradeoffs
Jumping to the Dashboard
Opening the complex ChatBot dashboard, navigating to 'Stories,' then clicking into 'Interactions,' and finally filtering by date and user ID. This process takes 5-10 minutes and requires multiple context switches.
→
Just ask your agent: 'Show me the interactions for Story X for User Y.' Your agent handles the multi-step query using list_story_interactions and get_chatbot_user_details automatically.
Guessing the Bot's Scope
Assuming that because the bot handles FAQs, it must know about billing. You spend time searching the 'Knowledge Base' only to find the required data point is missing.
→
Run list_chatbot_entities to see the exact data points the bot is trained to recognize. This immediately tells you if billing codes or account numbers are part of its current scope.
Manual User Data Collection
Needing to manually check if a user changed their email address. You have to find the user's ID in a spreadsheet and then log into the CRM to check their profile.
→
Use list_chatbot_users to find the user ID, and then run get_chatbot_user_details to pull the latest profile data directly into your chat session.
When It Fits, When It Doesn't
Use this server if your primary need is deep, programmatic visibility into the performance and data of a conversational bot. You need to know who talked to the bot, what they said, and where the bot failed. Don't use it if you just need to send a simple message or trigger a basic action (like 'send an email'). For those, a simple messaging tool works. If you need to build a complex data pipeline that uses this data (e.g., sending user data to Salesforce), you'll likely need to combine the results from list_chatbot_users and list_story_interactions in a custom backend. This server gives you the raw inputs; you still need to write the orchestration code.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ChatBot. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Monitoring bot performance shouldn't require opening a dedicated dashboard.
Today, figuring out why a bot failed is a nightmare. You have to jump between the bot platform's dashboard, the user log files, and the internal knowledge base. You're clicking tabs, exporting CSVs, and copy-pasting IDs just to build a basic performance report.
With the ChatBot.com MCP Server, you just ask your agent. You prompt it: 'What happened with user X in the Product FAQ story?' Your agent calls `list_story_interactions` and `get_chatbot_user_details` and returns the whole story—the failures, the messages, the context—right in your chat.
ChatBot.com MCP Server: Get full bot audit data.
Previously, gathering training data required manually sifting through support tickets and finding those weird, unrecognized phrases. It was a huge, time-consuming job to figure out what the bot needed to learn.
Now, you just run `list_training_data`. The server pulls a clean list of every phrase the bot missed. You feed that list back into the training pipeline. Simple.
Common Questions About ChatBot.com MCP
How do I list all bot workflows using list_chatbot_stories? +
You call list_chatbot_stories to get a master list of every story. This tells you the names and IDs of all the conversational paths the bot knows how to run.
Can I check a user's full chat history using get_chatbot_user_details? +
No, get_chatbot_user_details gives you the user's profile (like name or email). To see the full chat history, you need to run list_story_interactions after identifying the correct story and user.
What is the best tool for finding out what the bot needs to learn? +
Use list_training_data. This tool specifically lists unrecognized phrases, which are the exact pieces of text you need to add to the bot's knowledge base.
How do I see what data points the bot recognizes? +
Run list_chatbot_entities. This tool lists all the custom entities the bot is trained to match in user input, such as specific product codes or names.
How do I list all users who have talked to the bot using list_chatbot_users? +
You call list_chatbot_users to get a list of every unique user who interacted with the bot. This tool returns basic user profiles, letting you count unique users or filter by date.
How do I find out which interactions happened in a specific bot story using list_story_interactions? +
list_story_interactions pulls every recorded interaction for a given story ID. You can review the full sequence of messages and identify exactly where the user deviated from the expected path.
What tools are available to check for required bot training phrases? +
Use list_training_data to retrieve all unrecognized phrases. This tool gives you a clean list of user input that the bot didn't match to any existing intent, pointing you directly to needed training.
How can I view the custom data points the bot uses for matching using list_chatbot_entities? +
list_chatbot_entities lists all custom entities configured in your account. This shows you what data the bot recognizes—like product codes or names—and lets you audit your NLP setup.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
LiveKit
Manage real-time video, audio, and data sessions via LiveKit — create rooms, manage participants, and trigger recordings directly from your AI agent.
Pushbullet
Send files, links, and notifications between your phone and computer instantly with universal copy-paste across all devices.
NumVerify
Validate and audit phone numbers — identify carriers and locations via AI.
You might also like
Kisi
Control building access with cloud-managed smart locks, mobile credentials, and audit trails that replace traditional key cards.
Humanloop (LLM Prompt Management API)
Manage, version, and deploy LLM prompts directly from your AI agent using the Humanloop API.
Peerbie
Orchestrate your entire team's workspace, from Kanban boards to calendar events, completely driven by AI.