How to Use the Hallucination Detector Prover MCP in Pydantic AI

Type-safe grounding for Pydantic AI. Ensure every agent claim is verified against your Pydantic schemas and sources.

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Connect Hallucination Detector Prover MCP to Pydantic AI

Create your Vinkius account to connect Hallucination Detector Prover 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 Hallucination Detector Prover with Pydantic AI

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Strict grounding for Pydantic AI

Your agent runs `validate_hallucination_grounding` to ensure every claim fits your defined truth-value. The tool rejects any response that lacks proper source attribution. This forces the agent to adhere to the epistemic standards you set. It turns loose text generation into a verifiable, evidence-based process.

Confidence-aware agents in Pydantic AI

The `validate_hallucination_grounding` tool assigns a confidence score to every assertion. It requires the agent to provide the evidence quality for each claim. If the agent lacks sufficient data, it must mark the confidence as low. This stops the model from masking uncertainty with authoritative-sounding filler.

Cross-check agent logic in Pydantic AI

Your agent uses `validate_hallucination_grounding` to find internal contradictions. It checks if subsequent sentences align with the initial factual claims. This ensures that your agent remains consistent throughout its entire response. It catches logic errors before they leave the pipeline.

Setup guide

Set up Hallucination Detector 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": {
        "hallucination-detector-prover-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Hallucination Detector Prover transactions")
print(result.output)

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Common questions about Hallucination Detector Prover MCP in Pydantic AI

You use the MCPToolset class to link the server to your agent. This allows the agent to call the grounding tool as part of its tool-use capabilities.

Yes, it validates outputs at runtime. If the agent makes an ungrounded claim, the tool catches it, allowing you to trigger a retry or a correction.

The tool returns a validation error. This prevents the agent from finalizing the output until it provides verifiable evidence or admits to a knowledge gap.

It complements your Pydantic models by verifying the content of the strings. Your schema handles the structure, and this tool handles the factual rigor.

The server is built for zero-trust environments. It processes the text in memory and discards it immediately, keeping your validated claims private.

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