How to Use the Close MCP in Pydantic AI

Q: How do I install the Close MCP Server for Pydantic AI?

Run pip install "pydantic-ai-slimmcp" in your environment. Initialize the connection using MCPToolset with the Vinkius HTTP URL and pass it to your agent.

Q: Why should I use Pydantic AI to manage my Close leads?

It guarantees type safety. If the getleaddetails tool returns unexpected data, the framework raises a runtime error instead of letting the agent hallucinate.

Build type-safe sales agents that validate Close CRM data at runtime using Pydantic AI and MCP.

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Connect Close MCP to Pydantic AI

Create your Vinkius account to connect Close 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 Close with Pydantic AI

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Type-Safe Lead Insertion

The `create_lead` tool lets your Pydantic AI agent insert new records without risking silent data corruption. The framework validates every field against strict Python types before sending it. If the CRM API returns an unexpected schema, the agent fails loudly with a validation error. This prevents corrupted lead data from entering your production CRM.

Validated Opportunity Tracking with Pydantic AI MCP Server

The `list_opportunities` tool fetches sales pipeline data directly into your typed code. Pydantic AI parses the API response into structured models so your agent can process deal values safely. This model-agnostic approach means you can swap LLM providers behind the scenes. You can run local models or commercial APIs, and the data returned by `get_lead_details` remains strictly validated.

CRM Task Validation

The `list_crm_tasks` tool helps your agent manage sales follow-ups without hallucinating due-dates. The agent checks tasks against strict date formats defined in your Python schemas. To set this up, run `pip install "pydantic-ai-slim[mcp]"` and initialize `MCPToolset` with the HTTP endpoint. Pass the toolset directly to your agent to auto-discover all Close tools.

Setup guide

Set up Close 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": {
        "close-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Close MCP in Pydantic AI

Run `pip install "pydantic-ai-slim[mcp]"` in your environment. Initialize the connection using `MCPToolset` with the Vinkius HTTP URL and pass it to your agent.

It guarantees type safety. If the `get_lead_details` tool returns unexpected data, the framework raises a runtime error instead of letting the agent hallucinate.

Yes. Pydantic AI is model-agnostic. You can run the Close tools with local models, OpenAI, or Anthropic while keeping the exact same validation rules.

This setup supports both Streamable HTTP and SSE transports. The server must be running externally, which Vinkius handles automatically.

Vinkius isolates the connection inside an ephemeral V8 sandbox. Your credentials, sales opportunities, and CRM tasks are never cached, logged, or exposed.

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