How to Use the Hallucination Detector Prover MCP in Mastra AI

Q: How do I add the Hallucination Detector Prover to Mastra AI?

Run npm install @mastra/mcp@latest. Initialize a new MCPClient and pass the MCP Server URL in the servers object. Mastra automatically detects if you are using Streamable HTTP or SSE.

Q: Does the Hallucination Detector Prover work with Mastra AI human-in-the-loop?

It pairs perfectly. You can set requireToolApproval on the tool execution. The agent attempts to verify its claims, and a human reviews the confidence scores before the workflow proceeds.

Q: What happens when an agent makes a claim outside its knowledge boundary?

The validatehallucinationgrounding tool flags it as a hallucination. The agent must explicitly state what it does not know. Guessing temporal or domain-specific facts results in an immediate rejection.

Catch fabricated data before it breaks your workflows. Enforce strict fact-checking in Mastra AI.

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Connect Hallucination Detector Prover MCP to Mastra AI

Create your Vinkius account to connect Hallucination Detector Prover to Mastra 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 Mastra AI

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Ground Workflows in Mastra AI

The `validate_hallucination_grounding` tool forces your agents to back up their claims with hard evidence. When a workflow generates a report, this tool steps in to audit the facts. It demands specific authors, publication dates, and URLs for every assertion. Complex multi-step operations fail when early steps rely on fabricated data. This MCP Server stops that chain reaction. If an agent invents a metric, the tool flags it as unverified. You can then use Mastra's conditional branching to retry the generation or notify an admin.

Separate Fact from Opinion

The `validate_hallucination_grounding` tool explicitly labels every statement your agent makes. It draws a hard line between independent, verifiable facts and subjective opinions. The model cannot pass off its own preferences as objective truth. Automated retries and exponential backoff won't fix bad reasoning. If an agent claims a specific API is the best choice without evidence, this MCP Server rejects it. The agent must acknowledge its knowledge boundaries, including training cutoffs and missing data access.

Stop Self-Contradicting Agents

The `validate_hallucination_grounding` tool scans the entire output payload for internal inconsistencies. It compares claim A against claim D. If the agent says an operation takes 50ms in step one and 200ms in step three, the tool throws an error. Long-running agents drift off track. They forget their initial parameters and start hallucinating numbers to fill the gaps. This tool catches those contradictions, forcing the agent to resolve the discrepancy before the workflow continues.

Setup guide

Set up Hallucination Detector Prover MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Hallucination Detector Prover tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "hallucination-detector-prover-mcp-client",
  servers: {
    "hallucination-detector-prover-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Hallucination Detector Prover Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Hallucination Detector Prover tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Hallucination Detector Prover transactions"
);
console.log(result.text);

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Common questions about Hallucination Detector Prover MCP in Mastra AI

Run `npm install @mastra/mcp@latest`. Initialize a new `MCPClient` and pass the MCP Server URL in the servers object. Mastra automatically detects if you are using Streamable HTTP or SSE.

Yes. When the tool rejects an ungrounded claim, it returns an error to the workflow. You can configure Mastra to catch that failure and retry the generation with stricter system prompts.

It pairs perfectly. You can set `requireToolApproval` on the tool execution. The agent attempts to verify its claims, and a human reviews the confidence scores before the workflow proceeds.

The `validate_hallucination_grounding` tool flags it as a hallucination. The agent must explicitly state what it does not know. Guessing temporal or domain-specific facts results in an immediate rejection.

The MCP Server only inspects the specific assertions and context strings you pass to the tool. Vinkius isolates this process in a zero-trust environment. No database stores your workflow outputs, and the memory clears completely after the run.

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