How to Use the Dripcel MCP in Pydantic AI

Q: How do I install and configure Dripcel with Pydantic AI?

Install the framework using pip install "pydantic-ai-slimmcp". Create an MCPToolset pointing to your Vinkius HTTP URL, and pass it to your Agent via the toolsets argument to instantly expose tools like sendwhatsappmessage.

Q: Does the Dripcel MCP server support the deprecated MCPServerHTTP client in Pydantic AI?

No. You should use the unified MCPToolset constructor instead, as MCPServerHTTP is deprecated. This ensures your Pydantic AI agent maintains a stable, long-lived connection to your WhatsApp instances over Streamable HTTP.

Q: Can I use Pydantic AI to validate WhatsApp templates before sending?

Yes. You can fetch templates with listwhatsapptemplates and use Pydantic AI's type-checking to ensure the parameters your agent wants to send match the exact variables required by the Dripcel template.

Q: How does Pydantic AI ensure the privacy of my WhatsApp messages?

Raw WhatsApp messages fetched via listwhatsappmessages are validated in-memory on your server. Pydantic AI does not cache or store this sensitive conversational data externally, keeping your customer communications private and compliant.

Run type-safe WhatsApp campaigns with Pydantic AI validating every message and contact schema.

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Connect Dripcel MCP to Pydantic AI

Create your Vinkius account to connect Dripcel 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 Dripcel with Pydantic AI

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Type-safe WhatsApp dispatch using Pydantic AI

Stop worrying about your agent sending corrupt payloads to WhatsApp. Pydantic AI enforces strict runtime validation, ensuring that every arguments block passed to `send_whatsapp_message` matches the exact string and parameter schemas required by Dripcel. If your agent tries to inject invalid fields into a message template, the framework fails loudly before the API call is made. This guarantees that only clean, valid data gets sent, saving you from API errors and blocked numbers.

Bulletproof contact parsing and search

Extracting contact details from raw LLM outputs is notoriously brittle. By integrating this MCP server, Pydantic AI maps the JSON responses from `search_instance_contacts` and `list_instance_contacts` directly into strongly-typed Python models. Your code can safely access attributes like phone numbers and names without checking for missing fields. The framework guarantees that the data returned by `get_instance_details` matches your defined types, making your backend workflows incredibly reliable.

Strict health audits without silent failures

Monitoring connection states requires absolute accuracy. Use `quick_instance_health_audit` to fetch the status of all your WhatsApp lines, knowing that Pydantic AI will validate the connection states at runtime. If `get_whatsapp_connection_status` returns an unexpected payload, the agent won't hallucinate a fix. It raises a validation error immediately, allowing your system to trigger alerts or switch to backup instances safely.

Setup guide

Set up Dripcel 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": {
        "dripcel-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Dripcel MCP in Pydantic AI

Install the framework using `pip install "pydantic-ai-slim[mcp]"`. Create an `MCPToolset` pointing to your Vinkius HTTP URL, and pass it to your `Agent` via the `toolsets` argument to instantly expose tools like `send_whatsapp_message`.

Pydantic AI validates every JSON response against strict Pydantic models. If a tool like `get_instance_details` returns a field that doesn't match the schema, the framework raises a validation error rather than letting your agent process corrupt data.

No. You should use the unified `MCPToolset` constructor instead, as `MCPServerHTTP` is deprecated. This ensures your Pydantic AI agent maintains a stable, long-lived connection to your WhatsApp instances over Streamable HTTP.

Yes. You can fetch templates with `list_whatsapp_templates` and use Pydantic AI's type-checking to ensure the parameters your agent wants to send match the exact variables required by the Dripcel template.

Raw WhatsApp messages fetched via `list_whatsapp_messages` are validated in-memory on your server. Pydantic AI does not cache or store this sensitive conversational data externally, keeping your customer communications private and compliant.

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