How to Use the Serviceform MCP in LangChain

Q: How does Serviceform improve lead flow when paired with LangChain?

LangChain agents use getlead and listforms to build structured funnels. Instead of just gathering data, the agent decides how to gather it, passing context from one tool output to the next.

Q: What types of chats can I query with Serviceform and LangChain?

You use listchats to pass criteria as a JSON string. This means you're not limited; you can filter histories based on any specific data point you need for your multi-step reasoning.

Q: Does Serviceform help with understanding site structure when used with LangChain?

Yes, by running listspaces followed by getspaceitems, the agent builds a complete map of what content exists on the site. This structured data is easy for your chain to consume.

Q: Where does Serviceform store the captured lead details?

The server captures and exposes detailed records via getlead and listleads. These are specific, actionable pieces of customer contact data.

Run multi-step conversion funnels with LangChain's structured agent logic.

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Setup Serviceform with LangChain

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Chaining Leads and Forms via MCP Server

The `get_lead` tool lets your agent pull specific lead details. You can chain this directly: first, list all available forms using `list_forms`, then pass the resulting form IDs into a call to `get_lead`. This sequence ensures your ReAct agent knows exactly which data points to look for when building its next step. This capability lets you build complex reasoning pipelines. The output of checking active forms becomes the input context for fetching individual lead records, making sure no potential data point is missed.

Listing and Managing Conversational Assets

Use `list_chatbots` to see every chatbot configured on Serviceform. After listing them, you can call `list_chats`, passing a criteria JSON string to filter specific conversation histories. This two-step process allows your agent to narrow down the scope of what it needs to analyze. This is perfect for creating full observability chains. The agent first gets the list of all chatbots available and then uses that knowledge base to query only the relevant chat history records.

Identifying Website Structure with MCP Server

The `list_spaces` tool shows every flex space on Serviceform. Once you know the spaces, you can run `get_space_items` to pull all specific items associated with a chosen area. This gives your agent a full understanding of the content structure. This sequence is critical for multi-server aggregation. You first get the blueprint (the list of spaces) and then execute the detailed data retrieval using the item getter, ensuring the subsequent steps in your chain have all necessary context.

Setup guide

Set up Serviceform 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 Serviceform 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({
    "serviceform-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 Serviceform 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 Serviceform. 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 Serviceform MCP in LangChain

LangChain agents use `get_lead` and `list_forms` to build structured funnels. Instead of just gathering data, the agent decides *how* to gather it, passing context from one tool output to the next.

You use `list_chats` to pass criteria as a JSON string. This means you're not limited; you can filter histories based on any specific data point you need for your multi-step reasoning.

Yes, by running `list_spaces` followed by `get_space_items`, the agent builds a complete map of what content exists on the site. This structured data is easy for your chain to consume.

The server captures and exposes detailed records via `get_lead` and `list_leads`. These are specific, actionable pieces of customer contact data.

LangChain supports stateless operation by default, but you can use `client.session()` to maintain persistent context across multiple tool calls, keeping your reasoning pipeline consistent.

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