How to Use the Delivery Integrity Prover MCP in Pydantic AI

Q: How does Delivery Integrity Prover enforce correctness for a Pydantic AI agent?

It forces the agent to perform a final check using the verifydelivery MCP tool. The tool's structured response is then validated by your Pydantic models, ensuring both the agent's work and its self-assessment are correct before you proceed.

Q: What if verifydelivery's output doesn't match my Pydantic model?

Pydantic AI will raise a ValidationError in your Python code. This is a good thing—it means there's a mismatch between what you expect and what the agent provided, and it stops your program before that bad data can cause problems.

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Setup Delivery Integrity Prover with Pydantic AI

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Get Validation You Can Trust

The `verify_delivery` tool gives you a clear, structured response: a boolean verdict and a list of any outstanding issues. With Pydantic AI, this response is automatically parsed and validated against your Pydantic models at runtime. If the agent's self-assessment is malformed or incomplete, your code raises a `ValidationError` immediately. You aren't just hoping the agent did the right thing; you have verifiable proof baked into your type system. No more silent failures.

Fail Loudly with Pydantic AI

Pydantic's philosophy is to fail fast and loudly when data is wrong. This tool brings that same rigor to agent behavior. When your agent calls `verify_delivery` and the work isn't actually done, the tool returns a definitive `False`. Your Pydantic AI agent sees that boolean, and you can build logic that stops execution right there. The agent is prevented from continuing based on a false premise. It forces correctness at the task level, not just the data level.

A Consistent Check for Any LLM

Pydantic AI lets you switch between models from OpenAI, Anthropic, or even local providers. The problem is, they all have different failure modes and quirks. This MCP server tool gives you a single, consistent quality bar for all of them. It doesn't matter which backend model is driving your agent. Before any task is considered complete, it has to pass the exact same `verify_delivery` checklist. It's a universal standard of accountability for your model-agnostic agent.

Setup guide

Set up Delivery Integrity Prover 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": {
        "delivery-integrity-prover-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Delivery Integrity Prover transactions")
print(result.output)

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Common questions about Delivery Integrity Prover MCP in Pydantic AI

It forces the agent to perform a final check using the `verify_delivery` MCP tool. The tool's structured response is then validated by your Pydantic models, ensuring both the agent's work and its self-assessment are correct before you proceed.

Pydantic AI will raise a `ValidationError` in your Python code. This is a good thing—it means there's a mismatch between what you expect and what the agent provided, and it stops your program before that bad data can cause problems.

Absolutely. The tool is model-agnostic. As long as your Pydantic AI agent can call the MCP Server endpoint, it can use `verify_delivery` to check its work, regardless of whether it's powered by a cloud API or a model running on your own machine.

No, they work together. Pydantic validates the structure of your data. Delivery Integrity Prover validates the correctness of your agent's actions against a set of instructions. You need both.

Your data, including task requirements and validation logs, is processed within a zero-trust environment on Vinkius. Your Vinkius token handles authentication, and every MCP call runs in a completely ephemeral instance that is destroyed afterward.

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