How to Use the Texter MCP in AutoGen

Q: What is the role of Texter departments in AutoGen conversations?

Agents can list available departments using listtexterdepartments. This allows them to route messages or analyze chat context based on pre-defined organizational units during their debate.

Q: If I use the Texter API in AutoGen, what data types are involved?

The agents manage chat metadata, department IDs, and message history. The final decision relies on synthesizing all this structural information to take action.

Q: Does AutoGen require specific chat IDs when interacting with Texter?

Yes. Most tools require a target ID. Agents first use listtexterchats to gather available chat IDs, which they then pass to the action-taking agent.

Force consensus on messaging strategies using AutoGen.

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Setup Texter with AutoGen

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Perplexity

Debate the best message for a lead.

You set up multiple agents to argue over outreach. One agent might call `list_texter_chats` to gather data, while another calls `add_label_to_texter_chat` to categorize risk. The final decision—the action taken by the system—is based on their debate and consensus.

Simulate follow-up campaigns with multiple views.

AutoGen can simulate complex communication flows. Agents negotiate which template message to use, calling `send_texter_template` for a WhatsApp push versus calling `send_texter_message` for a standard SMS drip.

Validate chat status before resolution.

A process can't just close chats. One agent calls `get_texter_chat_details` to check the full history and metadata. A second agent uses that data to decide if it's safe to call `resolve_texter_chat`, ensuring proper procedure.

Setup guide

Set up Texter MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Texter tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Texter_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Texter data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Texter_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Texter data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

The agents work together. One agent might suggest a message, and another calls `send_texter_message` to execute it once consensus is reached. It's decision-driven messaging.

Yes. You can set up an agent that reviews the chat history and calls `add_label_to_texter_chat` to apply a standardized tag, making sure all agents agree on the labeling structure.

Agents can list available departments using `list_texter_departments`. This allows them to route messages or analyze chat context based on pre-defined organizational units during their debate.

The agents manage `chat metadata`, department IDs, and message history. The final decision relies on synthesizing all this structural information to take action.

Yes. Most tools require a target ID. Agents first use `list_texter_chats` to gather available `chat IDs`, which they then pass to the action-taking agent.

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