How to Use the Texter MCP in Pydantic AI

Q: Can Pydantic AI list all available Texter departments?

Yes. You use listtexterdepartments to retrieve a clean, validated list of all department IDs and names the system supports.

Q: When should I use templates versus plain text with Texter and Pydantic AI?

If you're sending a WhatsApp or Messenger message, use sendtextertemplate. If it's a simple SMS drip campaign, the general sendtextermessage tool is fine.

Q: How can I verify chat existence with Texter and Pydantic AI?

You first list all chats using listtexterchats. Then, use gettexterchatdetails to get specific, validated information on the conversation.

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Connect Texter MCP to Pydantic AI

Create your Vinkius account to connect Texter to Pydantic AI 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 Pydantic AI

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Perplexity

Send messages using Pydantic AI

When your agent needs to send a simple text follow-up, it calls `send_texter_message`. Because of the MCP Server's structure, the response is always validated against defined Python models. This means if the API returns garbled data, your agent fails loud and clear—no silent corruption. Correctness comes first.

Review chat history with Pydantic AI

Need to see what was said before? Use `list_texter_messages` to get a message log for any given chat ID. The data structure is immediately validated by your framework. This ensures that the historical context provided to your agent is always predictable and usable.

Manage conversation states with Pydantic AI

Use `add_label_to_texter_chat` to assign labels after a campaign or interaction. The function takes parameters that are validated, giving you confidence in the data being written back. Your agent can then use `list_texter_departments` to understand organizational context.

Setup guide

Set up Texter MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "texter-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Texter tools.",
)

result = await agent.run("List recent Texter transactions")
print(result.output)

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Common questions about Texter MCP in Pydantic AI

The MCP Server works by exposing tools that are wrapped in type-safe models. This means every time your agent uses a function like `send_texter_message`, the input and output types are guaranteed.

The server handles messaging content, chat IDs, labels, and department names. The data type is structured communication metadata.

Yes. You use `list_texter_departments` to retrieve a clean, validated list of all department IDs and names the system supports.

If you're sending a WhatsApp or Messenger message, use `send_texter_template`. If it's a simple SMS drip campaign, the general `send_texter_message` tool is fine.

You first list all chats using `list_texter_chats`. Then, use `get_texter_chat_details` to get specific, validated information on the conversation.

Use it with your favorite AI tools

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