Landbot MCP. Manage every step of the customer conversation flow.
Landbot MCP lets your AI client manage entire conversational funnels from start to finish. You can pull detailed customer records, view full chat histories, send automated follow-up messages, or instantly hand off a conversation from a bot to a live agent—all without leaving your current workspace. It gives your agents the ability to act directly within complex customer support pipelines.
Give Claude and any AI agent real-world access
View a list of every bot operating within the Landbot system.
Fetch detailed information about one particular bot by its unique ID.
Search for a specific customer profile using an email address.
Pull core details and metadata for a single identified customer.
Download the complete chronological transcript of messages exchanged with a customer.
Programmatically reroute an active bot dialogue and assign it to a live human agent.
Send a structured message directly into the customer's ongoing chat thread.
Ask an AI about this
Waiting for input…
What AI agents can do with Landbot: 8 Conversation Tools
Use these tools to interact with customer data, list bots, retrieve message transcripts, or assign agents directly from your AI client.
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 Landbot MCPAssign Agent
Programmatically hands off a conversation thread from the bot to a live human agent.
Get Bot
Retrieves specific details for one Landbot instance using its unique identifier.
Get Customer
Fetches the core profile and metadata for a single customer account.
Get Messages
Pulls the complete, chronological transcript of all messages exchanged with a...
List Bots
Lists every Landbot active in your organization's account.
List Customers
Retrieves a list of the most recently interacting customers with the bots.
Search Customers
Searches for and locates a specific customer profile using their email address.
Send Text Message
Sends an automated message directly into the ongoing conversation with a customer.
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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Landbot, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
The Struggle of Context Switching
Today, understanding a single customer's journey requires painful context switching. You start in the primary chat tool to see their last message, then you jump to your CRM dashboard to verify if they are paying customers, and finally, you open an internal knowledge base just to find out which bot was supposed to handle them. This manual process means critical details—the payment status or the specific funnel name—are often missed in the rush.
With this MCP, all that data lives within your AI agent’s scope. You simply ask for a customer's profile and their chat history simultaneously. Your agent retrieves the metadata (`get_customer`) *and* the full transcript (`get_messages`), delivering one single answer that gives you 100% context right when you need it.
Landbot MCP: Take Action, Not Just Notes
The biggest waste of time is gathering data only to have to switch back to a UI button to act on it. You pull up the full history and see the bot failed to resolve the issue; you then have to manually change the ticket status, find the right human team, and write an internal note about the handoff.
Now, your agent handles the action for you. After reviewing the chat log, you simply tell it to route the conversation using `assign_agent`. The MCP takes care of updating the system, changing the ownership, and notifying the correct person—all from a single prompt.
What Landbot MCP does for your AI
Managing customer conversations used to mean jumping between five different dashboards just to get context: one for chat history, one for lead status, another for active bots, and maybe a fourth just to send a quick follow-up text. This MCP changes that. It connects your AI agent directly into the Landbot system, letting it see and manipulate live conversations as if it were a human operator sitting at the desk.
Your agent can search for specific customers by email or pull up an entire chat sequence log to understand exactly what happened before you got involved. If the conversation is complicated, your agent can programmatically reassign that thread directly to a specialized human agent using simple commands. Plus, it lets you send automated text messages right into the active dialogue channel.
Because this MCP lives in the Vinkius catalog, your AI client connects once and gets access to all these critical communication tools for your whole operation.
019d75c4-4c78-711d-a87b-9a3179f9989d How to set up Landbot MCP
The bottom line is: you use natural language prompts in your AI client instead of logging into multiple service dashboards.
First, you authorize your AI client connection through Vinkius and provide the Landbot API token in the credentials.
Next, you instruct your agent to perform a specific action, like fetching chat logs or rerouting an interaction. The MCP executes that command against the Landbot system.
Finally, your agent receives structured data—whether it's the customer's full message history, their profile details, or confirmation that the conversation has been assigned to Sarah.
Who uses Landbot MCP
This MCP is for Operations Engineers, Sales Marketers, and Product Managers who are tired of manually hopping between customer support tools to get a complete view of a single interaction. You need immediate context, reliable data retrieval, and the ability to programmatically change the flow when needed.
On a Tuesday afternoon, you use this MCP to retrieve a customer's full chat history (get_messages) before calling them. This ensures your conversation starts with the correct context and shows the customer that you actually read their previous messages.
When an AI bot collects a lead, you use this MCP to search for that contact's record (search_customers) and pull their complete profile details before triggering the next sales sequence. This prevents wasted effort on unqualified leads.
You examine conversation flows by listing all active bots (list_bots) to understand which customer journeys are being run. You can then use get_customer data to map out common friction points in the user path.
Benefits of connecting Landbot MCP
Instantly see full context. Instead of asking a customer, 'What were we talking about?', your agent runs get_messages and has the entire chat transcript ready to analyze and respond to, ensuring zero friction in service.
Control the handoff process. If the AI can't solve it, you don't want the conversation to stall. Use assign_agent to immediately route a complex ticket to a human agent, guaranteeing continuity of care.
Deep customer profiling. Need more than just a name? Running get_customer retrieves key metadata about the account, letting your agent tailor responses based on subscription level or purchase history.
Targeted outreach. You can't wait for a lead to come back online. Use send_text_message to send a specific follow-up message directly into their chat channel when they need it most.
Operational oversight. By using list_bots, your team can quickly audit which conversational pathways are running and check the status of every bot without logging into separate monitoring dashboards.
Landbot MCP use cases
A sales rep needs to qualify a new lead.
The rep doesn't want to rely on memory. They prompt their agent to search for the prospect by email using search_customers. The agent instantly pulls up the contact’s full profile, allowing the rep to immediately reference specific details—like which bot they interacted with last week—and ask highly targeted questions.
Support needs to escalate a complex technical issue.
The initial chatbot interaction failed because it hit a knowledge gap. The support agent tells their AI client to run get_messages for the customer ID, pulling up every single message exchanged. This history allows the agent to understand the full scope of failure and then use assign_agent to hand off the case with a perfect summary.
Product team needs to analyze chat bottlenecks.
The product manager wants to know why customers drop off. They run list_bots first, identifying all active funnels. Then they use get_bot on a specific bot and cross-reference that data with customer profiles (list_customers) to pinpoint exactly where the conversation fails or gets stalled.
Marketing needs to re-engage an inactive lead.
The marketing team identifies high-value leads who went silent. They use get_customer to pull up their last known details, then instruct the agent to send a personalized message using send_text_message, restarting the conversation loop without human intervention.
Landbot MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Searching for customer data manually
The user opens the chat interface, scrolls up through dozens of messages to find a specific detail, and then has to copy that information into an internal CRM ticket. This takes five minutes per client.
Don't scroll or copy anything. Ask your agent to run get_customer first. The tool pulls the metadata instantly and provides it in clean, structured format for immediate use.
Assuming a bot is still running
A developer assumes that because they set up a new bot matrix, it's live and functional when testing. They waste time manually checking multiple internal dashboards to verify its status.
Before you test anything, always run list_bots. This confirms if the system sees the bot as active and available for use before you start running complex simulations.
Trying to fix a conversation without history
A support agent jumps in chat with 'Hi, how can I help?' but realizes they have no idea why the customer is upset because they didn't pull the log first. This leads to frustration and repeat questioning.
Always start by calling get_messages for that customer ID. The full transcript provides immediate context so your agent can address the root problem right away.
When to use Landbot MCP
Use this MCP if your workflow involves managing, analyzing, or acting upon conversational data—specifically chat transcripts and lead funnel status. If you need to know who a customer is, what they said, where they are in the pipeline, or if an automated message needs to be sent, this MCP provides the necessary tools (get_messages, search_customers, send_text_message). Don't use it if your goal is simple data retrieval that doesn't involve a conversation context. For instance, if you just need a list of all employee names from an HR database, you need a general CRM tool, not one designed for chat flow control. You must be focused on the lifecycle of customer interaction.
Frequently asked questions about Landbot MCP
How do I use Landbot MCP to see all my active bots? +
You run list_bots to get an inventory of every bot running in your account. This is the first step if you need to audit or understand what conversational pipelines exist.
What information does Landbot MCP provide about a customer? +
You can use get_customer to pull core profile details and metadata on a single individual. You also run search_customers if you only know their email address.
Can I send messages through the Landbot MCP? +
Yes, you can use send_text_message to programmatically inject an automated reply into a customer's live chat conversation. This is useful for follow-ups or confirming information.
How do I make sure a difficult chat gets to a human? +
Use the assign_agent tool. Your agent intercepts the dialogue and routes it away from bot control, assigning ownership to a live team member immediately.
Is Landbot MCP only for viewing data? +
No, it's much more than that. You can perform actions like sending messages and reassigning agents, making it an active control layer over your customer service system.