Hallucination Detector via Consistency Connector for AI agents.
3 live capabilities
Verify factual accuracy and catch AI contradictions
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Why people use Hallucination Detector via Consistency
Stop AI hallucinations with Hallucination Detector via Consistency
This MCP changes that by automating the skepticism. Instead of you doing the heavy lifting, you let the capability break the text down and look for the cracks. You get a clear signal on whether the information is stable or if the agent is starting to wander into fiction.
What Vinkius changes
You get a mathematical way to prove whether your AI is telling the truth or just guessing.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Verifying historical data extraction
An analyst uses extract_claims to pull dates from old documents and then checks them for consistency to ensure no errors were introduced during the process.
- Real-world use case 02
Testing prompt stability
A developer uses analyze_consistency to see if changing a prompt causes the agent to start hallucinating different facts.
- Real-world use case 03
Automated content auditing
A content team uses identify_contradictions to scan large batches of AI-generated articles for conflicting claims about a product's features.
Complete set · 3capabilities
The complete Hallucination Detector via Consistency capability set.
These are the exact actions your AI can choose when you ask it to work with Hallucination Detector via Consistency.
01—03
3 capabilities in this set.
Part of 3 available through Hallucination Detector via Consistency.
- 01 Capability
Extract claims
Splits raw text into individual, verifiable facts. This makes it easy to check specific details like dates or amounts.
- 02 Capability
Analyze consistency
Compares multiple responses to see if they agree. It gives you a high-level view of how reliable the answers are.
- 03 Capability
Find contradictions
Compares sets of claims to identify logical conflicts
Set up in minutes
One URL. Then ask Hallucination Detector via Consistency to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Hallucination Detector via Consistency from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Hallucination Detector via Consistency, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Hallucination Detector via Consistency for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Hallucination Detector via Consistency URL.
- Step 03
Save and start
Save the connection and enable Hallucination Detector via Consistency in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"hallucination-detector-via-consistency": {
"url": "https://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Hallucination Detector via Consistency
Open Agent mode in chat and ask: "Using Hallucination Detector via Consistency, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"hallucination-detector-via-consistency": {
"url": "https://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Hallucination Detector via Consistency
Ask Copilot: "Using Hallucination Detector via Consistency, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"hallucination-detector-via-consistency": {
"url": "https://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Hallucination Detector via Consistency
Open Cascade and ask: "Using Hallucination Detector via Consistency, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"hallucination-detector-via-consistency": {
"url": "https://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Hallucination Detector via Consistency
Ask Cline: "Using Hallucination Detector via Consistency, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add hallucination-detector-via-consistency --transport http "https://edge.vinkius.com/vk_preview_hkgY7Z5J8UbQd7NqclEuQsXU1SpnqkJ6HHDo9KPz/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Hallucination Detector via Consistency
Ask Claude: "Using Hallucination Detector via Consistency, show me...". 3 tools are ready
Where the request belongs
Work Hallucination Detector can move forward.
This is for anyone building automated workflows where accuracy isn't optional. It's for the developers and researchers who can't afford to let a hallucination slip into a final report or a production database.
AI Engineer
Testing the reliability of new prompts or model versions by checking for consistency in outputs.
Data Analyst
Verifying that extracted data from unstructured text remains consistent across different extraction runs.
Content Auditor
Checking large volumes of AI-generated content for factual errors or logical leaps.
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Bring your own AI
Change the model, client or framework. Keep Hallucination Detector connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
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LM Studio -
AnythingLLM -
Open WebUI -
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Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Hallucination Detector.
The practical details behind the request, access and result.
How can I use Hallucination Detector via Consistency to check my AI's work?
You can provide multiple responses from your AI client to this MCP, and it will compare them to see if they agree on the facts, highlighting any discrepancies it finds.
Can Hallucination Detector via Consistency find errors in long documents?
Yes. It can break down long pieces of text into individual facts and then check those facts for any logical contradictions or conflicting numbers.
Does Hallucination Detector via Consistency work with any AI client?
Yes, it works with any MCP-compatible client like Claude, Cursor, or Windsurf, as long as you have it connected through Vinkius.
What makes Hallucination Detector via Consistency different from a standard prompt?
Standard prompts ask an AI to be right; this MCP uses a structured, deterministic approach to verify if the AI is being consistent across different attempts or different pieces of text.
Is Hallucination Detector via Consistency useful for data extraction?
Absolutely. It's highly effective for ensuring that when you extract numbers, dates, or names from a document, the information remains consistent and accurate.
How does the capability detect hallucinations?
It uses analyze_consistency to compare multiple LLM responses. If the responses provide conflicting dates, numbers, or entities, the consistency score drops, flagging the output as potentially unreliable.
What kind of data can be extracted?
The extract_claims capability specifically targets dates, numeric values, and named entities to ensure the analysis remains deterministic and verifiable.
Can I adjust the sensitivity of the detection?
Yes, when using analyze_consistency, you can provide a custom threshold to make the detection more strict or more lenient.
How does the consistency score work?
The score is calculated by subtracting the ratio of unique contradictions to the total number of extracted claims from 1.0. A score of 1.0 means perfect agreement.
What can I do with `analyze_consistency`?
You can use analyze_consistency to pass a list of multiple LLM responses and receive a report containing a consistency score and indices of suspect responses.
Can I customize the strictness of the detection?
Yes, you can provide a custom threshold value to analyze_consistency to define when a set of responses should be flagged as inconsistent.
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