How to Use the Faker MCP in Pydantic AI

Q: Can I use Faker with custom schemas in Pydantic AI?

Yes. The getcustom tool lets you define field mappings, which you can then validate against your existing Pydantic models.

Strictly typed mock data for your Pydantic AI applications.

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Connect Faker MCP to Pydantic AI

Create your Vinkius account to connect Faker 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 Faker with Pydantic AI

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Validate Faker outputs with Pydantic AI

Every tool response is checked against your defined models at runtime. If the `get_custom` output doesn't match your schema, the agent stops immediately, preventing silent data corruption. This gives you total confidence in your agent's input. You define the structure, and the framework enforces it, making your test code significantly more reliable.

Use Faker in a type-safe environment

You get full IDE completion for every tool output. When you call `get_products`, the framework treats it as a validated object, not a loose dictionary. This eliminates hallucinated fields in your agent's logic. You can build complex, multi-step workflows where each stage relies on the strict output of the previous tool call.

Deploy this MCP Server with Pydantic AI

Connect your agent to the server using the MCPToolset class. It supports both SSE and HTTP transports, ensuring a stable connection for your production test suites. Because the server runs externally, you can update your mock logic without redeploying your agent. It's the cleanest way to handle test data for enterprise-grade AI applications.

Setup guide

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

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

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

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Common questions about Faker MCP in Pydantic AI

The framework validates every response against your Pydantic models. If the server returns unexpected data, the agent fails before the error propagates.

Yes. The `get_custom` tool lets you define field mappings, which you can then validate against your existing Pydantic models.

The toolset handles the protocol overhead, including retries and connection management. It's built for persistent use in automated test environments.

No. The server runs as an independent process. You just point your toolset to the Vinkius URL and it handles the communication.

The server generates synthetic, non-PII data for testing. It doesn't access your local files or network, ensuring that your test data remains completely anonymous and secure.

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