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Hallucination Detector Prover

Hallucination Detector Prover MCP for AI. Make your AI outputs fact-checked, source-cited, and accountable.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
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Works with every AI agent you already use

…and any MCP-compatible client

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Connect to your AI in seconds.

Hallucination Detector Prover forces rigorous accountability on AI outputs. This MCP checks if your agent can actually back up its claims by demanding verifiable sources, separating opinion from fact, quantifying confidence levels, and spotting internal contradictions in the text.

It ensures that everything generated is grounded in evidence, not plausible fiction.

What your AI can do

Validate hallucination grounding

This MCP forces the AI to validate its output by checking for sources, separating facts and opinions, quantifying confidence, stating knowledge limits, and catching internal contradictions.

Verify Source Citation

Forces the AI to cite specific authors, publications, and DOIs for every factual claim it makes.

Calibrate Confidence Levels

Requires the agent to assign a confidence metric based on how strong the supporting evidence is (e.g., peer-reviewed study vs. blog post).

Separate Fact from Opinion

Labels statements as either independently verifiable facts or subjective opinions.

Declare Knowledge Limits

Makes the AI state what its knowledge cutoff date is and what domains it cannot cover.

Check Internal Consistency

Scans the entire output to flag contradictions between different sections or claims.

Included with Plan

Waiting for input…

AI Agent

Hallucination Detector Prover: 1 Tool

You can use the single tool available here to validate an AI's output by forcing source attribution, confidence calibration, and consistency checks.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Hallucination Detector Prover on Vinkius

Validate Hallucination Grounding

This MCP forces the AI to validate its output by checking for sources, separating facts and opinions, quantifying confidence, stating...

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Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The Hallucination Detector Prover integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

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Make Your AI Do More

Start with Hallucination Detector Prover, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,100+ others, all in one place
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  • Works with Claude, ChatGPT, Cursor, and more
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Hallucination Detector Prover MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hallucination Detector Prover. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 1 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The problem is accepting information without proof.

Today, you copy an AI-generated summary and paste it into a presentation. You read 'Industry adoption rates are rising rapidly,' but there's no citation. The next slide says 'Competitor X dominates the market.' It sounds persuasive, but if anyone asks for the source data or sees contradictory figures later in the report, your whole argument falls apart because you accepted unverified claims.

With this MCP, your agent is forced to prove every single point. Before it even suggests a stat, it has to link it to an original study and quantify how certain that evidence makes it. You get answers built on verifiable guardrails.

Hallucination Detector Prover: Verified Facts Only

The manual steps of finding sources, checking for contradictory data points across sections, and verifying that the author actually stated it are gone. You don't have to manually check if 'Smith et al.' in paragraph one conflicts with a later claim.

Now, when your agent gives you an output, you know it passed five levels of rigorous scrutiny. The risk is entirely shifted from human error or model overconfidence to verifiable evidence.

What your AI can actually do with this

When you need to trust what an LLM says—say, for a client report or medical summary—you can't just accept it. You need proof. This MCP forces your AI client to prove its work by demanding five things: specific citations for every fact, a confidence score based on evidence quality, clear labeling of opinion versus verifiable data, and an explicit statement about what the model doesn't know.

It also cross-references the entire output to catch when different parts contradict each other. By connecting this MCP through Vinkius, you make sure your agent operates with genuine epistemic rigor. You stop getting answers that sound right but are completely fabricated.

Built · Hosted · Managed by Vinkius Hallucination Detector Prover - Fact-Checking MCP
Server ID 019e6514-2375-72ab-9e51-2bcb88292e49
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How does the Hallucination Detector Prover MCP work? +

It checks for five types of AI errors, including missing sources and internal contradictions. It makes sure every claim is tied back to verifiable evidence.

Does validate_hallucination_grounding check if the facts are true? +

No, it doesn't verify truth in a vacuum. Instead, it forces you to provide sources and checks for internal contradictions within the text provided by your agent.

Can I use Hallucination Detector Prover MCP on long documents? +

Yes. The tool's cross-referencing capability is designed to check consistency across multiple paragraphs, which is key for long or complex reports.

Is the confidence quantification part of validate_hallucination_grounding mandatory? +

Yes. It requires the AI agent to assess and quantify its own confidence level based on the quality of the evidence it used.

How do I set up my agent to use validate_hallucination_grounding? +

You just activate the tool within your AI client's settings. You don't need special API keys; Vinkius manages the connection through your existing account credentials.

What kind of input does validate_hallucination_grounding prefer? +

It handles raw text inputs fine, but providing context or structured claims helps the analysis. The tool is built to analyze textual assertions regardless of how they were originally formatted.

What happens if I pass a prompt that lacks sources to validate_hallucination_grounding? +

The MCP doesn't error out; it reports the failure mode back to you. It will specifically trigger and flag SOURCE_MISSING, pinpointing exactly where evidence is needed.

Is my proprietary content secure when running validate_hallucination_grounding? +

Yes, Vinkius processes your data securely. Your input prompts and results are handled according to strict privacy protocols; they are not used for general model training.

What counts as a verifiable source? +

Author or organization, publication name, date, and DOI or URL. 'Studies show' is rejected. 'Smith et al., Nature 2024, doi:10.1038/...' is accepted.

How does confidence calibration work? +

The engine requires per-claim confidence with evidence quality: '90% confident (3 peer-reviewed sources)' instead of 'definitely' or '100% certain'.

Can it detect self-contradictions? +

Yes. It rejects circular self-validation like 'as I said' and demands explicit cross-referencing by paragraph and claim number.

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