How to Use the Follow Up Boss MCP in LangChain

Q: Can LangChain trigger a Follow Up Boss webhook?

You can inspect active triggers by calling listconfiguredwebhooks. From there, your agent decides how to format its output to match the expected payload of those hooks.

Q: Does the Follow Up Boss MCP integration support LangSmith tracing?

Yes. Every time your agent calls createnewlead or listfollowuptasks, LangSmith logs the inputs, outputs, and execution time automatically.

Build ReAct agents in LangChain that pull real estate leads and push tasks right back into Follow Up Boss.

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Connect Follow Up Boss MCP to LangChain

Create your Vinkius account to connect Follow Up Boss 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 Follow Up Boss with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Follow Up Boss MCP Server tools

LangChain agents treat your CRM as just another node in a data pipeline. You pull a raw inquiry using `list_leads` and pass that output directly into an LLM for qualification. If the lead fits your criteria, the chain fires off `create_followup_task` so your human agents know exactly who to call first. You get full visibility into what the agent actually does. LangSmith traces the latency of `get_lead_details` and logs the exact token usage for every decision. The agent decides the order of operations based on the intermediate data it sees, keeping your real estate workflow moving without manual data entry.

Track events and append notes

Your custom agent can monitor incoming website activity by calling `list_recent_events`. When a high-value prospect views a specific property listing multiple times, LangChain triggers a sequence. It grabs the context and immediately executes `add_internal_note` on the profile. This stops your team from constantly refreshing dashboards. Instead of waiting for a broker to notice a spike in activity, your ReAct agent logs the behavior using `log_lead_activity` and queues it up for review. You build the logic, and the agent executes the busywork.

Connect showings to real estate pipelines

You can build a graph that correlates calendar data with closed revenue. The agent pulls scheduled showings via `list_calendar_appointments` and cross-references them against `list_real_estate_deals`. It figures out which property types are actually moving to the closing table. Because LangChain supports multi-server aggregation, you can map these MCP endpoints alongside your vector stores. The agent reads the deal status, checks past interactions with `list_person_notes`, and updates the pipeline without you writing a single line of integration code.

Setup guide

Set up Follow Up Boss 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 Follow Up Boss 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({
    "follow-up-boss-alternative-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 Follow Up Boss 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 Follow Up Boss. 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 Follow Up Boss MCP in LangChain

Install the langchain-mcp-adapters package. Initialize a MultiServerMCPClient, call client.get_tools(), and pass the resulting array directly into your create_agent setup.

You can inspect active triggers by calling `list_configured_webhooks`. From there, your agent decides how to format its output to match the expected payload of those hooks.

Yes. Every time your agent calls `create_new_lead` or `list_followup_tasks`, LangSmith logs the inputs, outputs, and execution time automatically.

LangChain agents are stateless by default. You need to use client.session() to keep persistent context if you are running multiple operations on a single lead profile.

Vinkius runs the server in an isolated V8 sandbox. When your agent pulls transaction details via `list_real_estate_deals`, the connection is ephemeral and zero-trust. We never store your commission splits, property addresses, or client names on our infrastructure.

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