How to Use the TrueReview MCP in Pydantic AI

Q: Does TrueReview make campaign listing simple with Pydantic AI?

Yes. When you use listcampaigns, your agent gets a list of campaigns that strictly adheres to the defined data schema. You'll never get unexpected fields or types.

Q: How does TrueReview handle contact data with Pydantic AI?

The server provides listcontacts and getcontact. Crucially, both endpoints guarantee that the returned contact information will validate perfectly against your Python models. Correctness is guaranteed.

Q: Can I send a request to a customer with Pydantic AI?

You can. The sendreviewrequest tool validates every parameter upfront, meaning the action you trigger is built on solid data foundations.

Q: If I have questions about Locations using TrueReview and Pydantic AI, how do I get that info?

Use listlocations. This tool returns location details that are guaranteed to match the expected Pydantic structure. You'll know exactly what you're working with.

Guarantee data accuracy and type safety when managing reviews with Pydantic AI.

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Connect TrueReview MCP to Pydantic AI

Create your Vinkius account to connect TrueReview 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 TrueReview with Pydantic AI

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List all campaigns using the MCP Server.

When you run `list_campaigns`, your agent expects a strictly defined list of campaign objects. If the API returns anything unexpected, the Pydantic validation fails loud and clear. No silent data corruption. This lets you confidently build pipelines knowing exactly what structure the campaign list will take.

Send review requests via Pydantic AI.

The `send_review_request` tool ensures that when you send a request, all required parameters (like contact ID and location) are validated against your defined models. You don't get garbage outputs. This reliability is key when building mission-critical agents that need to execute actions perfectly.

Get immediate performance summaries with TrueReview.

Calling `get_report_summary` provides a structured review report. Because of Pydantic, if the backend data is malformed, your agent doesn't crash; it fails with an explicit validation error. This means you always know when the input data for reporting is wrong.

Setup guide

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

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

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

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Common questions about TrueReview MCP in Pydantic AI

Yes. When you use `list_campaigns`, your agent gets a list of campaigns that strictly adheres to the defined data schema. You'll never get unexpected fields or types.

The server provides `list_contacts` and `get_contact`. Crucially, both endpoints guarantee that the returned contact information will validate perfectly against your Python models. Correctness is guaranteed.

The MCP Server handles Contacts, Locations, Campaigns, and Review Data. Every piece of returned data passes validation checks before your agent even sees it.

You can. The `send_review_request` tool validates every parameter upfront, meaning the action you trigger is built on solid data foundations.

Use `list_locations`. This tool returns location details that are guaranteed to match the expected Pydantic structure. You'll know exactly what you're working with.

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