How to Use the Salesquared MCP in Pydantic AI

Q: How do I setup Salesquared with Pydantic AI?

Install the slim MCP package and use the MCPToolset class. Provide the server URL to your agent definition, and the framework will handle the HTTP/SSE transport automatically.

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Connect Salesquared MCP to Pydantic AI

Create your Vinkius account to connect Salesquared 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 Salesquared with Pydantic AI

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Type-safe lead creation in Pydantic AI

The Salesquared MCP Server returns structured data that your Pydantic AI models validate at runtime. When your agent calls `create_lead`, the response is checked against your schema immediately. If the server returns malformed data, your agent raises a validation error. You catch these issues before they corrupt your database or trigger downstream logic.

Validated messaging for Pydantic AI

Use `send_sms` to dispatch alerts with confidence. Your agent defines the expected output types, and the framework forces the tool output to match those types exactly. This prevents silent failures in your communication pipeline. You ensure every message payload is correct before it reaches the carrier gateway.

Voice survey tools for Pydantic AI

Launch surveys with `create_voice_survey` while maintaining strict type safety. Your agent passes the survey parameters, and Pydantic AI verifies the request structure before it hits the server. This makes your voice automation predictable. You avoid runtime errors by defining the expected shape of survey objects, ensuring your agent always sends valid parameters.

Setup guide

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

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

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

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Common questions about Salesquared MCP in Pydantic AI

Install the slim MCP package and use the `MCPToolset` class. Provide the server URL to your agent definition, and the framework will handle the HTTP/SSE transport automatically.

Pydantic AI will throw a validation error. This prevents your agent from processing bad data, keeping your application state consistent and avoiding dangerous hallucinations.

Yes. Because it uses the MCP standard, you can swap your underlying LLM without changing your tool definitions. The server works the same regardless of whether you use OpenAI or local models.

Data privacy is enforced by the sandbox environment. Your lead records, including sensitive PII, are transmitted over encrypted channels and are never stored on the MCP host server.

Yes, provided you handle the runtime validation errors. The framework is designed for production where you need to know immediately if a tool response violates your type constraints.

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