Chatsistant MCP for AI. Manage your entire chat bot ecosystem via conversation.
Works with every AI agent you already use
…and any MCP-compatible client








Connect to your AI in seconds.
Chatsistant connects your chatbots to any AI client, letting you manage the entire conversation flow through natural dialogue. You can list all bots, review every chat session, and even train them by adding new knowledge sources—all without logging into a dashboard.
It gives developers and ops teams total control over their chatbot ecosystem from where they already work.
What your AI can do
Add data source
Adds a new URL, document, or text snippet to train a specific chatbot.
Get bot
Retrieves the detailed profile and status for one particular bot.
Get conversation
Fetches all messages and details from a single, specific chat session.
List every configured bot, checking its status and knowledge base settings.
Retrieve the complete message history for any specific conversation across any connected bot.
Programmatically add new URLs, documents, or text snippets to expand what a bot knows.
Query any active bot and receive an answer generated specifically from its trained data sources.
View all configured webhooks, checking which event triggers them and how they send data.
Ask an AI about this
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Chatsistant MCP: 8 Tools for Bot Management
These eight tools give you granular control over listing bots, managing data sources, reviewing conversations, and querying bot knowledge in a conversational way.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Chatsistant on VinkiusAdd Data Source
Adds a new URL, document, or text snippet to train a specific chatbot.
Get Bot
Retrieves the detailed profile and status for one particular bot.
Get Conversation
Fetches all messages and details from a single, specific chat session.
List Bots
Provides an overview listing every chatbot currently configured in the system.
List Conversations
Lists recent conversations, with an option to filter by which bot was involved.
List Data Sources
Shows all the external data sources currently feeding information into a specific bot.
List Webhooks
Lists every webhook setup, detailing its event triggers and delivery settings.
Query Bot
Sends a question to the bot and gets an answer based on its current knowledge base.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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 Chatsistant, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chatsistant. 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 connection provides 8 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with Bot Documentation is a Clickfest
Right now, adding new information—whether it's an updated FAQ or a change in policy—means logging into the bot platform. You find the data upload section, zip up your documents, select the correct bot, and hit submit. Then you wait for it to process, hoping you didn't miss a required step.
With this MCP, the whole thing becomes conversational. Instead of clicking through multiple menus, you just tell your agent, 'Add our new FAQ page.' You get the confirmation back immediately, knowing that data is now available for querying.
The Chatsistant MCP Gives You Full Visibility
Previously, reviewing a chat session meant navigating through conversation logs and then manually checking the bot's knowledge base settings to understand why it responded that way. It was disjointed.
Now, you can `get_conversation` details, review the exact message flow, and immediately check which data sources were used for that specific reply. You finally connect the 'what happened' with the 'why it happened'.
What your AI can actually do with this
Managing customer-facing chatbots used to mean jumping between dashboards, digging through logs, or manually updating data feeds. Now, you treat your entire bot deployment like another service managed by conversation. You can list all the bots running and inspect exactly what they know, seeing their knowledge bases in real time.
Need to debug a tricky interaction? Just review any chat session's full message history, no matter which bot was involved. If a bot needs more info, you don't upload files via a web portal; your agent simply adds the new data source for you. This ability makes it simple for developers to configure bots and monitor complex integrations across different channels using Vinkius' catalog of tools.
019dd0cd-18cf-7299-9312-e46a463a6df4 Here's how it actually works
The bottom line is you get total control over complex bot management without touching a graphical user interface.
Subscribe to this MCP and plug in your Chatsistant API Key from the dashboard.
Connect it via your preferred AI client (like Claude or Cursor).
Start managing bots, reviewing conversations, and adding data sources using natural language prompts.
Who is this actually for?
This MCP is for the Customer Experience Manager who spends too much time clicking through separate analytics tools to figure out why a chatbot failed. It's for the developer who needs to manage dozens of bot configurations and data sources without writing boilerplate code, or the Operations Engineer monitoring webhooks across multiple internal systems.
Reviews chat transcripts using list_conversations to spot common knowledge gaps and improve response quality.
Manages bot configurations and adds data sources via the agent instead of navigating a complex web dashboard.
Verifies that webhook connections are firing correctly and tracks bot connectivity across all business integrations.
What Changes When You Connect
Stop hunting through dashboards. You can list all bots and use get_bot to check specific configurations, all without clicking a single button.
Deep dive into every interaction using the MCP. list_conversations lets you pull full chat histories so you can spot exactly where the bot failed or succeeded.
Never manually update knowledge bases again. You use add_data_source to programmatically feed new documentation (URLs, files) directly to a bot's memory.
Testing answers is simple. Use query_bot to send questions and get instant, factual responses based only on the data sources you provided.
Full operational visibility means knowing if your integrations are live. Run list_webhooks to verify every event trigger fires correctly.
See it in action
Diagnosing a Bad Answer
A customer support agent sees a bot gave the wrong return policy. They ask their agent, 'Show me all conversations for the Support Bot last week.' The agent uses list_conversations to find the bad chat and then checks the data sources using list_data_sources to see if the correct policy was ever added.
Updating Policies
The marketing team just launched a new pricing page. Instead of logging into three different systems, they ask their agent, 'Add the FAQ and API docs URL to the Internal Wiki bot.' The agent executes add_data_source instantly.
Checking System Health
The ops team suspects a payment webhook is broken. They immediately run the check with list_webhooks. If it shows no recent activity, they know exactly where to focus their troubleshooting efforts.
Initial Bot Audit
A new client signs up and needs an audit of all existing bots. They ask their agent to 'List every bot I run.' The agent uses list_bots to give them a clean, comprehensive overview.
The honest tradeoffs
Forgetting the source
The user runs a query and gets an answer, but has no way of knowing if that answer came from old documentation or a newly added file.
Always pair query_bot with list_data_sources. This ensures you know exactly which knowledge base the bot used to generate the response.
Treating bots as silos
The user tries to manage the internal wiki, sales helper, and support agent by logging into three separate vendor dashboards.
Use list_bots first. This gives you a single view of all your agents so you can manage them cohesively from one place.
Assuming connectivity
A system fails and nothing seems wrong, but the user doesn't know if the failure was in the bot logic or the data delivery.
Check both list_webhooks and get_bot. This helps separate whether the issue is a broken integration (webhook) or bad configuration (bot settings).
When It Fits, When It Doesn't
Use this MCP if your primary pain point involves coordinating actions across multiple distinct chat bots, requiring you to manage their knowledge bases and conversation history programmatically. You need to know what the bot knows (list_data_sources) and how it answered a question (query_bot). Don't use this if you only need basic logging or simple message sending; those tasks are better handled by dedicated messaging tools. If you just want to see a conversation, list_conversations works fine, but if you need to diagnose the reason for the answer, you must check data sources and bot configurations.
Questions you might have
How do I list all my bots using the Chatsistant MCP? +
You use list_bots to get an overview of every bot. This tool returns a simple, actionable list showing the names and basic status of all your deployed chatbots.
Can I check old chats with list_conversations? Is it hard? +
No, you just use list_conversations to see recent history. You can even narrow down the search by telling it which specific bot ID was involved.
If I need to train a bot on new PDFs, do I use add_data_source? +
Yes, add_data_source is the tool you need. It lets you programmatically feed the system any type of data—URLs, files, or plain text—to expand the bot's knowledge.
How do I know if my webhooks are working? +
Run list_webhooks. This tool gives you a clear rundown of every configured webhook, showing exactly what event is supposed to trigger it and where the data should go.
What does query_bot actually do when I use it? +
It sends your question to a specific bot's knowledge base. The bot processes that question against its stored documents and returns an answer derived only from those sources.
When I use get_bot, what specific configuration details can I pull for a single chatbot? +
You get detailed status information and settings for that bot. This includes its unique ID, whether it's currently active or paused, and direct access to the names of all data sources attached to it.
If I use get_conversation, what kind of metrics can I pull from a specific chat session? +
You retrieve the full message history, allowing you to review who said what and when. It also provides key metadata about the conversation, like its total length or duration.
How do I audit all my current data sources using list_data_sources? +
This tool lists every knowledge base attached to your bots. You can check which source is associated with which bot and see the current status of that data set.
Can I send a question to a bot and get an AI-generated answer in real time? +
Yes! The query_bot tool accepts a Bot ID and a question string. It sends the query to the bot's AI engine and returns a response generated from its trained knowledge base — perfect for testing bot accuracy before deploying changes.
Can I review all the data sources currently training my bot? +
Yes. The list_data_sources tool returns all URLs, documents, and text snippets that have been added to a specific bot's knowledge base, including their processing status. Use add_data_source to programmatically add new URLs, text, or file content to expand the bot's training data.
Can I browse conversation histories across all my bots? +
Yes. Use list_conversations to retrieve all chat sessions — optionally filter by a specific Bot ID. Then use get_conversation with the Conversation ID to inspect the full message timeline, including user questions, bot responses, and timestamps.
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All 8 tools are live and waiting.
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