How to Use the ConnectAndSell MCP in Pydantic AI

Q: How do I register ConnectAndSell in Pydantic AI?

Install the package using pip install "pydantic-ai-slimmcp" and instantiate MCPToolset with your Vinkius HTTP URL to load the MCP Server. Pass this toolset into the toolsets list of your Pydantic AI Agent.

Q: How does Pydantic AI handle ConnectAndSell call histories?

Your agent queries listcallattempts using the MCP Server, and the framework validates the returned date formats and attempt logs. This guarantees that your historical analysis scripts only process clean data.

Run type-safe ConnectAndSell dialer workflows in Pydantic AI.

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Connect ConnectAndSell MCP to Pydantic AI

Create your Vinkius account to connect ConnectAndSell 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 ConnectAndSell with Pydantic AI

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Validate ConnectAndSell MCP Server metrics at runtime

The `get_performance_summary` tool fetches outbound calling metrics and validates them against strict Pydantic schemas before they reach your model. By registering your Vinkius MCP Server with `MCPToolset`, your Pydantic AI agent catches malformed dialer payloads instantly. If the dialer API returns a string where an integer was expected, the framework raises a validation error instead of letting your agent hallucinate a response. This keeps your sales reports accurate and predictable.

Type-safe outbound list retrieval

The `list_assigned_lists` tool retrieves prospect lists assigned for outbound calling sessions. Your Pydantic AI agent parses this list data, ensuring every prospect object conforms to your strict data models. This strict validation prevents your agent from passing corrupt contact records to downstream tasks. The agent handles these checks at runtime, working with any LLM provider you choose.

Secure CRM updates with strict schema checks

The `sync_to_crm` tool pushes dialer outcomes to your CRM using strict input validation. Your agent must supply a valid call ID and an approved CRM type, or the Pydantic AI framework blocks the tool execution. This mechanism prevents malformed sync requests from hitting your Salesforce or HubSpot instances. You get clean CRM pipelines without writing manual validation wrappers around your tool calls.

Setup guide

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

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

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

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Common questions about ConnectAndSell MCP in Pydantic AI

Install the package using `pip install "pydantic-ai-slim[mcp]"` and instantiate `MCPToolset` with your Vinkius HTTP URL to load the MCP Server. Pass this toolset into the `toolsets` list of your Pydantic AI Agent.

The framework raises a runtime validation error immediately, preventing the agent from processing bad dialer data. This ensures your python application fails loudly instead of silently corrupting your CRM.

Yes, Pydantic AI is model-agnostic, meaning your validated dialing tools will work with OpenAI, Anthropic, or local models. The tool schemas remain strictly enforced regardless of the backend model.

Your agent queries `list_call_attempts` using the MCP Server, and the framework validates the returned date formats and attempt logs. This guarantees that your historical analysis scripts only process clean data.

Your prospect lists, rep calling logs, and CRM credentials are never stored by Vinkius. Every tool call executes in an isolated V8 sandbox, passing encrypted payloads directly between your Python runtime and the dialer.

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