How to Use the Judge.me MCP in Pydantic AI

Q: How do I handle data privacy when routing feedback through this server?

When calling getreview, the payload contains physical shipping locations and reviewer IP addresses. You must define strict Pydantic models that intentionally drop these sensitive fields during validation so they never reach your application logs.

Build type-safe Pydantic AI agents that validate every Judge.me API response at runtime to guarantee predictable review moderation.

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Connect Judge.me MCP to Pydantic AI

Create your Vinkius account to connect Judge.me 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 Judge.me with Pydantic AI

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Claude

Perplexity

Enforce Strict Types on Store Data

LLMs hallucinate JSON fields. That breaks production systems. When your agent calls `get_product`, the framework validates the response against your exact schema. If the ID is a string instead of an integer, the agent fails loudly. You never get silent corruption. The agent pulls bulk data using `list_products` and maps every single item to your internal models. You get clean, typed data ready for downstream processing.

Type-Safe Q&A Management via MCP

Customer support requires exact answers. Your agent queries `get_question` to pull shopper inquiries. Because of the strict validation, you know the status field will exactly match your expected enums. Before drafting a reply, the agent checks `list_answers` to review previous support interactions. The framework guarantees that the agent only processes verified data structures. No missing timestamps. No null pointer exceptions.

Reliable Coupon and Settings Audits

You cannot afford errors when auditing discounts. The agent runs `list_coupons` to check active review incentives. Pydantic catches missing expiration dates immediately, preventing invalid codes from reaching your marketing reports. The same applies to store configuration. The agent reads `get_settings` to verify your widget layouts. If the API returns an unexpected nested object, the validation error tells you exactly which field broke the contract.

Setup guide

Set up Judge.me 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": {
        "judgeme-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Judge.me MCP in Pydantic AI

Install the slim package with MCP support. Initialize an MCP Toolset with your HTTP endpoint and pass it to your agent. Do not use the deprecated server classes.

Yes. The framework is model-agnostic. You can run a local model to parse `list_reviews` data if you want to avoid sending customer feedback to external providers.

A custom client requires manual maintenance every time the API changes. This server provides standard tools, and your Pydantic models ensure the data always matches your expectations.

Use Streamable HTTP or SSE transports. The server must be running externally, and these protocols maintain stable connections while the agent processes large datasets.

When calling `get_review`, the payload contains physical shipping locations and reviewer IP addresses. You must define strict Pydantic models that intentionally drop these sensitive fields during validation so they never reach your application logs.

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