How to Use the Postscript MCP in LangChain

Chain your SMS data into automated marketing workflows with LangChain and the Postscript MCP Server.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Postscript MCP to LangChain

Create your Vinkius account to connect Postscript to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Postscript with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build SMS pipelines with LangChain

Feed your agent subscriber data to trigger personalized outreach. You can chain `get_subscriber` with `update_subscriber` to keep Shopify profiles current without manual input. Your agent handles the logic between steps. It parses the output from `list_campaigns` to decide which message segment needs attention next.

Automate SMS compliance and webhooks

Use `create_webhook` to pipe opt-out events directly into your agent's decision loop. This ensures you never message a user after they stop their subscription. Monitoring your integration is simple. The chain records every tool call, letting you verify that `delete_webhook` executed exactly when needed.

Manage SMS marketing states

Control your entire subscriber lifecycle through code. Use `list_automations` to fetch active flows, then trigger specific actions based on the current revenue metrics. Logic flows happen in real-time. By passing output from `get_account_info` into your chain, the agent adjusts its behavior based on your current messaging limits.

Setup guide

Set up Postscript 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 Postscript 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({
    "postscript-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 Postscript 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 Postscript. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Postscript MCP in LangChain

Connect the MCP server to your agent using the provided adapters. Once connected, your agent calls `list_subscribers` to pull data directly into your processing chain.

Yes, configure your environment to receive incoming events and map them to agent tools. You use `list_webhooks` to verify your endpoint is active and ready to process updates.

Absolutely. You aggregate the server with other data sources in your agent definition to build complex pipelines that combine SMS data with your database records.

The agent catches the error and reports it in your trace logs. You can then define retry logic to re-run `update_subscriber` if the API hits a temporary rate limit.

All data stays within your local memory or specified storage. The MCP Server acts as a bridge, only passing the subscriber details your agent explicitly requests.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript