How to Use the Klipfolio MCP in Pydantic AI

Q: How do I define the Klipfolio tools in Pydantic AI?

You instantiate the MCPToolset with your server URL. Pydantic AI then automatically maps the tools to your agent, handling the type validation for you.

Q: Can I list individual Klips using Pydantic AI?

Call listindividualklips as a standard agent tool. The output is parsed into a Pydantic model, allowing you to access dashboard data as clean Python objects.

Use the Klipfolio MCP Server with Pydantic AI for type-safe, validated dashboard management.

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Connect Klipfolio MCP to Pydantic AI

Create your Vinkius account to connect Klipfolio 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 Klipfolio with Pydantic AI

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Validate Klipfolio data with Pydantic AI

Every response from `get_dashboard_details` is checked against a Pydantic model. If the API structure changes, the agent catches the mismatch immediately. This prevents your agent from hallucinating data. You get a reliable interface for your dashboard metrics that won't break when the API evolves.

Manage Klipfolio clients securely

Use `list_account_clients` to retrieve your workspace structure. The agent validates each client object at runtime to ensure your logic processes the correct account data. It brings strict structure to your dashboard management. You know the data you work with is exactly what you expect.

Trigger Klipfolio updates safely

Execute `refresh_data_source` with confidence. Pydantic AI validates the refresh confirmation response, ensuring your agent knows exactly when the update succeeds. You avoid silent failures in your reporting pipeline. It provides a clear, typed result for every refresh operation performed.

Setup guide

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

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

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

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Common questions about Klipfolio MCP in Pydantic AI

You instantiate the `MCPToolset` with your server URL. Pydantic AI then automatically maps the tools to your agent, handling the type validation for you.

It does. Because the framework performs runtime validation, any unexpected schema from Klipfolio triggers a validation error, preventing your agent from processing corrupt info.

Yes. Each tool call is strictly typed by the MCP protocol and validated by Pydantic, ensuring your agent arguments and return types always match your schema.

Call `list_individual_klips` as a standard agent tool. The output is parsed into a Pydantic model, allowing you to access dashboard data as clean Python objects.

The server uses your authenticated token to restrict access. Since the framework only operates on validated models, you prevent unauthorized data exposure and ensure that only expected fields are handled by your logic.

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