How to Use the HubSpot Analytics MCP in LangChain

Q: How can I use LangChain to compare web traffic and email performance from HubSpot Analytics?

Your agent can call hswebanalytics for traffic data and hsemailanalytics for campaign stats in the same chain. It can then analyze both outputs to tell you which channel is driving more valuable engagement.

Q: What's the best way to start building a HubSpot agent with LangChain?

Start with a simple goal, like fetching one metric. Use createagent and give it the tool list from this MCP server. A good first test is asking it to 'get the stats for my last marketing email' to see it use hsemailanalytics.

Q: Can my LangChain agent find a specific contact's activity in HubSpot Analytics?

Yes. The agent can use the hslistevents tool. You just need to provide the contact's ID, and the agent will return a timeline of their interactions, like page views and form submissions.

Build marketing data chains for your LangChain agents. Connect HubSpot Analytics and get real answers, not just metrics.

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Connect HubSpot Analytics MCP to LangChain

Create your Vinkius account to connect HubSpot Analytics 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 HubSpot Analytics with LangChain

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Chain HubSpot Tools for Deeper Analysis

Stop just pulling single data points. With LangChain, your agent can build a sequence of queries to find real insights. It can start by using `hs_list_reports` to find the name of a specific performance report, then pass that information to another tool in the chain to dig deeper. This isn't about running one-off commands. You build agents that reason. For example, an agent can check website performance with `hs_web_analytics`, notice a drop in new contacts, and then automatically decide to run `hs_email_analytics` on recent campaigns to find the cause. The output of one tool becomes the input for the next.

Create Agents That Audit Your Marketing

Give your agent a goal, not just a command. You can build a 'Health Check' agent that uses `hs_analytics_views` to see all available reporting scopes. From there, it can cycle through them, pulling traffic data with `hs_web_analytics` for each to spot anomalies. This approach lets you automate complex audit tasks. An agent can find a high-value contact, pull their entire history with `hs_list_events`, and then cross-reference that activity with email campaign performance from `hs_email_analytics` to score their engagement level.

Connect HubSpot Data to 500+ Integrations

This MCP Server doesn't operate in a vacuum. The data you pull from HubSpot can be the first link in a much longer chain. Your agent can get campaign stats with `hs_email_analytics` and then pass the results to a Google Sheet, a vector database, or another API entirely. LangChain makes it simple to combine this HubSpot data with other sources. You could have an agent check for new leads from `hs_web_analytics`, and if it finds any, immediately create a task in your project management tool. It's about connecting workflows, not just data.

Setup guide

Set up HubSpot Analytics 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 HubSpot Analytics 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({
    "hubspot-analytics-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 HubSpot Analytics 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 HubSpot. 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 HubSpot Analytics MCP in LangChain

Your agent can call `hs_web_analytics` for traffic data and `hs_email_analytics` for campaign stats in the same chain. It can then analyze both outputs to tell you which channel is driving more valuable engagement.

Start with a simple goal, like fetching one metric. Use `create_agent` and give it the tool list from this MCP server. A good first test is asking it to 'get the stats for my last marketing email' to see it use `hs_email_analytics`.

Yes. The agent can use the `hs_list_events` tool. You just need to provide the contact's ID, and the agent will return a timeline of their interactions, like page views and form submissions.

No, Vinkius handles that. You get a single endpoint and token for your MultiServerMCPClient. You don't have to worry about HubSpot's OAuth flow or refreshing tokens.

Your data is only processed for the duration of the API call. Vinkius uses ephemeral, sandboxed environments for each request. No HubSpot analytics data, like campaign metrics or contact event timelines, is ever stored.

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