Dashly MCP. Orchestrate entire customer journeys 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.
Dashly MCP connects your AI agent directly to your conversational marketing data. You'll manage user profiles, track custom actions, and respond to live chats without leaving your workflow.
Convert more website visitors by orchestrating targeted pop-ups and automated lead nurturing sequences.
What your AI agents can do
Get conversation
Retrieves all the details for one specific chat conversation.
Get user details
Fetches a complete profile summary and metadata for any user ID.
List channels
Lists every available communication channel set up in your Dashly account.
Retrieve detailed information about specific users, including custom properties and metadata.
Record custom events whenever a user performs an action on your website or app.
List recent conversations and fetch the full message transcript for any active session.
Write and send instant responses to a conversation thread directly through your AI client.
View all the different communication channels you use for lead engagement.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Dashly: 8 Tools for Customer Engagement
Use these tools to programmatically list users, read chat transcripts, record custom events, and update user metadata through your AI agent.
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 Dashly on Vinkius019dd0dcget conversation
Retrieves all the details for one specific chat conversation.
019dd0dcget user details
Fetches a complete profile summary and metadata for any user ID.
019dd0dclist channels
Lists every available communication channel set up in your Dashly account.
019dd0dclist conversations
Gets a list of all recent, ongoing chat sessions.
019dd0dclist users
Retrieves an overview of every registered user in your Dashly account.
019dd0dcsend reply
Drafts and sends a reply message to a specific conversation thread.
019dd0dcset user props
Manually updates or adds custom properties (metadata) to any user profile.
019dd0dctrack event
Records a specific, named action taken by a user, like 'pricing viewed'.
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 Dashly, 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 Dashly. 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.
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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.
Juggling Tabs to Track Leads Is Exhausting
Right now, if a lead gets confused about pricing, you might have them chat with support. You copy the conversation transcript into your CRM. Then you check the website analytics to see what pages they viewed before talking to support. Finally, you manually log that interaction in a spreadsheet to track their overall journey. It’s a mess of tabs and copy-pasting.
With this MCP, you just talk to your agent. You ask it to 'show me all activity for lead X'. The AI pulls together the chat history, the last recorded event (like 'pricing viewed'), and their profile metadata—all in one natural response. You stop doing manual work; you start getting instant context.
Send Messages When Context is King
The most painful part of support is having to re-read the last five messages just to decide what to say next. You lose momentum, and sometimes you miss a key detail that was mentioned in passing.
Now, your agent can access `get_conversation` details on demand. It knows exactly who said what, and when. Your replies are always informed by the full history, making every single interaction feel targeted and human.
What you can do with this MCP connector
This connection lets you treat your entire customer engagement system like a single chat session with your AI client. Instead of jumping between the Dashly dashboard, your CRM, and your analytics tool just to see what happened last week, your agent handles it all naturally. You can ask it for the full history on any user, track specific actions they take on your site—like viewing a pricing page—and even send them an instant reply directly through the system.
It’s about turning raw data into actionable conversations. Whether you're a support lead needing to quickly pull up a user's complete interaction history or a marketing manager who needs to see which users haven't signed up for that newsletter yet, your AI agent acts as the dedicated coordinator. This capability is managed through Vinkius, giving your agent access to thousands of other services too.
You get full control over your behavioral data and chat flow without ever manually toggling tabs or writing complex API calls.
019dd0dc-363f-7075-aec5-7f6dbfd366bb 
How Dashly MCP Works
- 1 Subscribe to this MCP and grab your Auth Token from your Dashly dashboard settings.
- 2 Connect that token to any AI-compatible client, like Cursor or Claude.
- 3 Use natural language commands with your agent. For example, ask it to 'list all users who viewed the pricing page' or 'get conversation history for user X'.
The bottom line is you use plain English instructions in your AI chat window instead of writing complex API code.
Who Is Dashly MCP For?
This MCP is for Ops Engineers and Marketing Managers who are tired of juggling multiple dashboards to figure out why a lead went cold. If you spend too much time manually cross-referencing user actions with chat logs, this connector saves your day.
Tracking custom events and managing the lifecycle of leads across multiple channels without leaving their primary workspace.
Instantly pulling up a user's complete activity profile, including all chat history and metadata, to give accurate support fast.
Monitoring engagement health by querying specific user properties or viewing usage trends across the entire customer base.
What Changes When You Connect
- Stop manually checking logs. Using
list_conversationsandget_conversationlets your agent pull up the full message history instantly, so you never miss a critical detail in support calls. - Keep a perfect record of user behavior by using
track_event. When a user views 'Pricing,' the system records that custom event immediately, building a rich audit trail for future campaigns. - Maintain clean data integrity with
set_user_props. Instead of relying on manual entry, you can update a user's status (like 'VIP') programmatically and keep your CRM in sync. - Get a bird’s-eye view by running
list_usersorget_user_details. You instantly pull profile metadata for an entire cohort or check one lead's specific custom properties. - Coordinate multi-channel outreach. By using
list_channels, you see all the ways users can engage, allowing your agent to plan a coordinated nurture sequence across touchpoints.
Real-World Use Cases
A support agent needs full context on a tricky lead.
The agent asks their AI client to 'get user details for john_doe'. The system runs get_user_details and returns that John Doe is tagged as 'High Value' and has 15 custom properties set. Then, they ask it to 'list conversations for john_doe', finding a chat where they can use send_reply with the right context.
Marketing needs to know who abandoned checkout.
Instead of building complex webhooks, the marketer tells their agent to 'track event Checkout Abandoned' for all users. The system runs track_event, and they can then query that data using list_users to build a targeted follow-up campaign.
Product team wants to audit user behavior.
The PM asks the agent to 'get all users who viewed the /settings page'. The tool uses track_event data, allowing the PM to segment and analyze which specific groups need better product education.
A sales rep needs to update a lead's status immediately.
After a call, the sales rep tells their AI client: 'update user_987 properties to Enterprise Tier'. The agent executes set_user_props, ensuring the CRM and Dashly are instantly in sync.
The Tradeoffs
Trying to replicate full funnel logic
Writing a complex script that tries to check every user, then query their chats, then track events for all of them. This is slow and brittle.
→
Let your agent handle the orchestration. Instead of writing code, simply ask: 'list users who haven't signed up yet.' The system handles the iterative checks using list_users combined with event data.
Ignoring existing user data
Assuming every lead is a fresh start and missing key behavioral context. This leads to generic, ineffective messaging.
→
Always check the history first. Use get_user_details or get_conversation before drafting any reply so your agent knows exactly what was said last.
Sending replies manually in a different tool
Seeing a conversation and having to copy the details into another system (like a spreadsheet) to plan a follow-up message.
→
Use get_conversation to pull up the full chat history, then immediately use send_reply to write and send your next action directly from the agent.
When It Fits, When It Doesn't
Use this MCP if your primary need is behavioral orchestration—connecting a user's website actions (events) with their support communications (chats). You need an AI client to read, write, and correlate data across multiple Dashly features. Don't use it if you only need to view basic contact lists; in that case, standard API connectors might suffice. If your goal is simple bulk reporting without any conversational context, a dedicated analytics tool is better. However, if the value comes from combining 'who they are' (get_user_details) with 'what they said' (list_conversations), this MCP is non-negotiable.
Common Questions About Dashly MCP
How do I check a user's current profile data using get_user_details? +
You simply ask your agent to 'get user details for [ID]'. It fetches the full metadata, including any custom properties you or your team have set up.
Can I track a new action in Dashly using track_event? +
Yes. You tell the agent to 'track event [Name]' for a specific user ID. This immediately records that custom behavior into their profile's behavioral history.
What is the difference between list_conversations and get_conversation? +
Use list_conversations to see an overview of all your recent chat sessions. If you need the full transcript, including every message sent, use get_conversation with a specific conversation ID.
How can I ensure my user data is accurate when I set_user_props? +
When setting user properties, be precise. Include the key and value clearly in your prompt. This ensures the system updates exactly what you intend for that specific user.
When I use send_reply, what happens if the message fails to deliver? +
If a reply fails, your agent receives an explicit error code. The system logs the reason for failure, letting you know if it's due to content restrictions or API limits. You can then adjust your script accordingly.
What information does list_channels provide about my communication setup? +
This tool provides a complete directory of all active connection points. It lists every channel Dashly uses, so you know exactly where to coordinate engagement across multiple touchpoints. Use this before connecting new services.
If I have thousands of users, how should my agent handle the list_users tool? +
The MCP is designed for large-scale data retrieval using pagination logic. Your agent will automatically prompt you to process results in batches, ensuring you get all user IDs without hitting rate limits.
When calling get_conversation, what specific ID format do I need? +
You must provide a precise conversation identifier (ID) for the tool to work. The agent can't guess; it needs that unique string to pull up the correct high-fidelity message history.
How do I find my Dashly Auth Token? +
Log in to your account, navigate to Settings > Integrations > API Keys, and copy your unique Auth Token.
Can I record custom user events via AI? +
Yes! The track_event tool allows your agent to log specific user actions programmatically by providing the user ID and event name.
How do I check active chat sessions? +
Use the list_conversations tool to retrieve a comprehensive directory of all current chat interactions and their high-fidelity metadata.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.