How to Use the Keap MCP in Pydantic AI

Q: Can I use Pydantic AI to search Keap contacts?

Yes. The agent uses listcontacts to find matches and parses the results into type-safe Python objects for safe processing.

Build type-safe Python agents with Pydantic AI that validate every Keap contact, tag, and invoice at runtime.

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Connect Keap MCP to Pydantic AI

Create your Vinkius account to connect Keap 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 Keap with Pydantic AI

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Type-Safe Contact Management in Pydantic AI

The `create_contact` tool registers new customers while enforcing strict runtime validation on incoming data. This ensures your Pydantic AI agent never writes malformed email addresses or incomplete names to your CRM. If the agent attempts to pass bad arguments, the framework blocks the call instantly. You get clean database records because the system catches formatting errors before they reach your live database.

Strict Tagging and Categorization

The `apply_tag_to_contact` tool tags specific profiles using validated tag structures. Your agent checks available tags with `list_tags` and applies them without risk of hallucinating non-existent categories. The framework validates the tag payload against your defined schemas at runtime. This prevents broken automation triggers that usually happen when agents make up tag names.

Validated Sales and Financial Auditing

The `list_invoices` tool fetches billing data and parses it directly into strict Python types. Your agent analyzes financial records knowing that every price, date, and invoice ID matches your schema exactly. It uses `list_orders` and `list_opportunities` to audit sales performance. The agent flags discrepancies immediately if any API response deviates from your expected data models.

Setup guide

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

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

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

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Common questions about Keap MCP in Pydantic AI

Install the slim MCP package and initialize the MCPToolset with your Vinkius HTTP URL. Pass this toolset directly to your Agent constructor.

The framework raises a validation error immediately. This stops the agent from processing bad contact profiles or corrupt invoice data.

Yes. The agent uses `list_contacts` to find matches and parses the results into type-safe Python objects for safe processing.

It supports both Streamable HTTP and SSE transports. You run the server externally and connect your Python code using a single endpoint token.

All API calls to retrieve contacts, tags, and invoices run inside temporary, isolated V8 sandboxes using this MCP Server. Your credentials and customer databases are never stored on our servers.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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