How to Use the Close CRM (Inside Sales CRM & Automation API) MCP in Pydantic AI

Q: Can I use Pydantic AI to filter Close CRM (Inside Sales CRM & Automation API) leads?

You can. The listleads tool supports pagination and field selection, which you can wrap in a Pydantic model for clean access.

Use Pydantic AI for type-safe interactions with Close CRM (Inside Sales CRM & Automation API) data.

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Connect Close CRM (Inside Sales CRM & Automation API) MCP to Pydantic AI

Create your Vinkius account to connect Close CRM (Inside Sales CRM & Automation API) 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 CRM (Inside Sales CRM & Automation API) with Pydantic AI

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Type-safe CRM operations in Pydantic AI

Every response from the server is validated against your models. If `list_contacts` returns malformed data, your agent stops before a single error propagates. You define the structure. The MCP Server ensures the data coming back from Close CRM (Inside Sales CRM & Automation API) matches your expectations exactly.

Validate lead updates via this MCP Server

When you call `update_lead`, your agent verifies the payload against your Pydantic schema. This prevents corrupt data from entering your system. You have total control over the data flow. It eliminates the risk of silent failures during your sales updates.

Schema discovery for Pydantic AI

Use `get_custom_field_schema` to understand your CRM layout. Your agent adapts its logic based on the actual fields present in your account. It makes your agent smarter about your specific sales setup. You get runtime safety for every interaction.

Setup guide

Set up Close CRM (Inside Sales CRM & Automation API) 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-crm-inside-sales-crm-automation-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Close CRM (Inside Sales CRM & Automation API) transactions")
print(result.output)

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Common questions about Close CRM (Inside Sales CRM & Automation API) MCP in Pydantic AI

The framework validates every server response against your Pydantic models. If the data doesn't fit, the agent raises a validation error immediately.

It is. You simply pass the MCPToolset to your agent and it handles the communication using typed models.

You can. The `list_leads` tool supports pagination and field selection, which you can wrap in a Pydantic model for clean access.

It supports both Streamable HTTP and SSE. You can choose the one that fits your deployment architecture best.

Your records are only accessed through strictly defined Pydantic-validated interfaces. This ensures no unauthorized or malformed data ever touches your CRM.

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