How to Use the Dashly MCP in LangChain

Q: How do I use Dashly tools in a LangChain agent?

You'll import MultiServerMCPClient and point it to your Vinkius endpoint. Call client.gettools() and pass the resulting list directly to your agent factory. LangChain handles the rest.

Q: Can I track custom user actions in Dashly with a LangChain agent?

Absolutely. The trackevent tool is designed for this. Your agent can decide when a significant action occurs in your app and log it directly to the user's Dashly profile.

Build support agents that chain Dashly actions. Perfect for creating complex, multi-step workflows in LangChain.

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Connect Dashly MCP to LangChain

Create your Vinkius account to connect Dashly to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dashly with LangChain

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Triage Support Tickets Automatically

This server gives your agent the tools to act like a front-line support rep. The agent can `list_conversations`, find the newest ones, and use `get_user_details` to see who it's from. Then, your LangChain agent can use its own logic—or even another tool—to decide what to do next. It can add context by calling `set_user_props` or track behavior with `track_event` before handing off to a human.

Craft Replies Based on User History

Your agent can pull a user's entire history to draft better replies. A chain might start with `get_conversation`, then `get_user_details`, and then check past events logged with `track_event`. With all that context, the agent can decide whether to `send_reply` with a helpful link or escalate the issue. You build the logic; the Dashly tools provide the data.

Manage Users with Your LangChain MCP Server

Go beyond just conversations. Your agents can build and maintain user profiles right inside Dashly. Use `list_users` to get a full roster, then drill down into specifics. This is great for maintenance tasks. An agent could find users with missing properties and update them using `set_user_props`, all driven by your LangChain logic. This MCP server handles the API calls.

Setup guide

Set up Dashly MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Dashly tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "dashly-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Dashly transactions"
    })
    print(result["messages"][-1].content)

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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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Common questions about Dashly MCP in LangChain

You'll import `MultiServerMCPClient` and point it to your Vinkius endpoint. Call `client.get_tools()` and pass the resulting list directly to your agent factory. LangChain handles the rest.

Yes, that's the point. The Dashly tools from this MCP server become available alongside any other LangChain integration, like a database or a vector store. You can build chains that cross all of them.

For simple chains, it's stateless. For longer interactions, wrap your client in `client.session()` to maintain context across multiple turns. This lets your agent remember which conversation it's working on.

Absolutely. The `track_event` tool is designed for this. Your agent can decide when a significant action occurs in your app and log it directly to the user's Dashly profile.

Yes. Your data—like user details and conversation content—is passed directly from the Dashly API to your LangChain agent through an encrypted Vinkius endpoint. Nothing is stored or logged by the MCP service itself.

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