How to Use the Texter MCP in LangChain

Q: How do I send messages using Texter with LangChain?

You use the sendtextermessage tool. The agent determines the target chat ID and message body, then executes the call as a step in your chain. It's simple; you just need to pass the correct parameters.

Q: Can LangChain list all Texter channels?

Yes. You simply execute listtexterchannels. This tool returns a list of every connected channel, letting your agent know what endpoints are available for messaging or data retrieval.

Q: What happens when I use resolvetexterchat with LangChain?

Calling resolvetexterchat tells the system that a conversation is finished. The agent can then record this status change, preventing it from being processed in future workflow steps.

Q: Does Texter support labeling chats with LangChain?

Absolutely. You use addlabeltotexterchat to apply labels. This is perfect for organizing leads after an automated campaign runs its course, keeping the data structured.

Q: What kind of message content can LangChain process from Texter?

Your agent processes everything: raw message content, chat metadata (labels, departments), and the initial channel list. The output is always a structured JSON object that your chain can read.

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Connect Texter MCP to LangChain

Create your Vinkius account to connect Texter 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 Texter with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate drip campaigns and follow-ups.

The `send_texter_message` tool lets your agent hit up a client with targeted SMS messages. You can build an entire multi-step sequence: first, use `list_texter_chats` to find all active conversations. Then, based on the chat status, you call `send_texter_message` to send the next follow-up in the drip campaign.

Manage and organize chats automatically.

Agents can maintain structure using Texter's metadata. Use `list_texter_labels` to see what labels exist, then call `add_label_to_texter_chat`. This lets the agent dynamically categorize a chat based on its content or status, which is key for reliable workflow chaining.

Gather and process message history.

When an agent needs context, it doesn't guess. It first calls `list_texter_messages` to pull the full chat transcript. This raw data feeds into a subsequent step where your model analyzes the conversation flow before deciding whether to call `get_texter_chat_details` for more information.

Setup guide

Set up Texter 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 Texter 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({
    "texter-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 Texter 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 Texter. 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 Texter MCP in LangChain

You use the `send_texter_message` tool. The agent determines the target chat ID and message body, then executes the call as a step in your chain. It's simple; you just need to pass the correct parameters.

Yes. You simply execute `list_texter_channels`. This tool returns a list of every connected channel, letting your agent know what endpoints are available for messaging or data retrieval.

Calling `resolve_texter_chat` tells the system that a conversation is finished. The agent can then record this status change, preventing it from being processed in future workflow steps.

Absolutely. You use `add_label_to_texter_chat` to apply labels. This is perfect for organizing leads after an automated campaign runs its course, keeping the data structured.

Your agent processes everything: raw `message content`, chat metadata (labels, departments), and the initial channel list. The output is always a structured JSON object that your chain can read.

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