AI Ethics Prover MCP for AI. Prove your model is safe, fair, and compliant before deployment.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
AI Ethics Prover forces your agent to conduct operational, quantifiable ethical reviews for any AI system. It demands named stakeholders, quantified risks, audited bias metrics, transparent decision paths, and defined appeal processes, preventing vague 'ethics washing' reports from compliance teams.
What AI agents can do with AI Ethics Prover Automation
Validate ai ethics
This tool forces a comprehensive and operationally specific ethical analysis of an AI system across five required axes: stakeholder identification, harm quantification, bias auditing, transparency demonstration, and recourse mechanisms.
It forces your agent to check an AI system against five operational standards (stakeholders, harms, bias, transparency, and recourse) before it passes the ethical review.
It requires you to score potential damages using severity ratings, probability percentages, and population size estimates.
It demands specific protected attributes and named statistical metrics (like parity or equalized odds) instead of accepting general claims of 'bias checking'.
It ensures the AI system can provide plain-language explanations that include counterfactual examples, showing exactly how a decision would change if one variable were different.
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What AI agents can do with AI Ethics Prover: 1 Tool Available
Use these tools to force an agent to run a comprehensive, structured ethical audit across five critical axes of any AI system.
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Start using AI Ethics Prover on VinkiusValidate Ai Ethics
This tool forces a comprehensive and operationally specific ethical analysis of an AI system across five required axes: stakeholder...
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
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.
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Open a new chat. The AI Ethics 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.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with AI Ethics 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
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AI Ethics 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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Built on the Model Context Protocol (MCP) for 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.
Compliance checklists feel great until they hit reality., Solved with Vinkius AI Gateway
Most companies handle AI ethics by creating compliance documents filled with buzzwords: 'fairness,' 'transparency,' and 'due diligence.' These reports are easy to write, but they fail because they rely on vague language. They say the system is 'potentially harmful' or that bias was 'checked generally.' You end up spending weeks in manual review sessions just trying to define what those terms even mean.
With this MCP, your agent forces specificity. It turns abstract compliance goals into concrete data requirements. Instead of accepting general claims, you must name the exact protected attribute, provide a specific detection metric, and score every potential harm with severity and probability. You get definitive proof, not just good paperwork.
AI Ethics Prover: Quantifying Ethical Proof
The most time-consuming part of manual auditing is tracking the five axes—stakeholders, harms, bias metrics, explainability, and recourse. Each one requires different types of evidence, forcing teams to jump between legal documents, data science reports, and product specifications. It’s a messy copy-paste cycle.
Now, you feed all that information into this MCP. The tool manages the complexity, checking every required component against an operational standard. You don't just get a 'pass' or 'fail'; you get a precise breakdown of exactly which element—be it transparency or recourse—needs fixing and how to fix it.
What your AI can actually do with this
When you claim an AI model is fair or transparent, those words mean nothing unless they are backed by hard data. The AI Ethics Prover changes that standard. It doesn't just accept a declaration of fairness; it forces your agent to prove the ethical soundness of any system across five core axes: who gets impacted, how bad is the harm, what bias metrics were used, how transparent are the decisions, and what happens when things go wrong.
You can connect this MCP through Vinkius's catalog to make sure that before you deploy a high-risk model—say, for lending or hiring—you have concrete evidence of compliance. It’s an analytical gatekeeper, demanding specific methodologies instead of vague assurances.
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Here's how it actually works
The bottom line is: you get an actionable failure report that points to the exact compliance gap, not just a vague 'needs improvement.'
You feed your agent an AI system's description or compliance statement that needs vetting.
The MCP executes the full analysis, forcing the agent to address all five ethical axes (stakeholders, harms, biases, transparency, and recourse).
It returns a detailed verdict matrix showing exactly which axis failed and what specific information is missing (e.g., 'Needs a defined SLA' or 'Missing protected attribute').
Who is this actually for?
This MCP is essential for Chief Risk Officers (CROs) and Compliance Managers who need proof of ethical standards. It's also critical for ML Engineers building high-stakes models, like those used in finance or healthcare, and Product Managers accountable for AI deployments.
Using this MCP, they run pre-deployment audits to ensure the model meets regional regulations (like the EU AI Act) by demanding documented proof of recourse and bias mitigation.
They feed the tool their model's technical specifications to prove that the system is auditable, detailing metrics like statistical parity and defining the data required for transparency demonstrations.
They use this MCP during product design reviews. If an AI feature affects job applicants or loan eligibility, they run the audit to proactively identify compliance risks before launch.
What Changes When You Connect
It moves you past vague 'ethics statements.' Instead of accepting that a system is 'fair,' the tool demands proof by forcing quantification of harms and naming specific protected attributes.
You get actionable compliance gaps. If an analysis fails, it doesn't just say 'fail'; it names the exact axis (e.g., HARMS_UNQUANTIFIED) and what data you must provide to fix it.
It provides a structured audit trail for regulators. The tool forces documentation of complex elements like counterfactual examples and defined appeal processes, which is crucial for high-risk sectors.
You avoid the legal risk of 'ethics washing.' By mandating explicit details on things like measured disparity and acceptable thresholds, you prove your due diligence was rigorous.
It standardizes internal review. Instead of relying on different departments writing disparate compliance documents, this MCP enforces one single, measurable ethical checkpoint.
See it in action
Auditing a Loan Decisioning Model
The compliance team needs to check their new lending model. They use the tool and provide input stating the system is 'fair.' The agent immediately rejects this, demanding specific metrics (like statistical parity) for race or income bracket, forcing the engineers to quantify disparities before approval.
Reviewing an Internal Hiring Tool
A Product Manager wants to launch an AI résumé screener. They run the MCP and find that while bias was audited, the transparency section fails because the system only says 'based on qualifications.' The tool forces them to define specific top-3 decision factors and create counterfactual examples.
Compliance for Healthcare AI
A hospital group is deploying an AI that predicts patient risk. They use the MCP to ensure proper recourse mechanisms are in place, requiring a structured challenge channel with a defined SLA and human reviewer role before going live.
The honest tradeoffs
Treating Ethics as Documentation
The team writes a 10-page report saying the AI is ethical, mentioning 'users' are affected and that bias was checked generally. This looks good on paper but lacks measurable data.
Don't rely on written reports alone. Use the validate_ai_ethics tool to force quantified input: name specific groups instead of 'society,' define metrics for bias, and score harms with severity (1-5).
Ignoring Recourse
The team claims that if a user is unhappy, they can just 'contact support.' This offers zero actionable path or timeline.
Run the audit through validate_ai_ethics. It forces you to define concrete recourse: an appeal process, a human reviewer role, and a specific Service Level Agreement (SLA).
Only Checking Bias
The team runs a tool that only checks for demographic parity but ignores the overall impact of poor transparency or unquantified harm.
Use validate_ai_ethics. It forces you to check all five axes—bias, plus stakeholders, harms, transparency, and recourse—in one go.
When It Fits, When It Doesn't
You need this MCP if your AI system falls into a high-risk category (finance, hiring, healthcare) or if regulatory compliance is paramount. Use it when you must prove how the model works and who it affects, not just that it works. Don't use this if your goal is simple text generation, basic data classification, or internal process automation that doesn't make decisions with real-world impact. If all you need is to check a database record or send an email, this MCP is overkill. You only need the rigorous audit when you are dealing with systemic risk and regulatory scrutiny.
Questions you might have
Does the AI Ethics Prover MCP replace legal advice? +
No, this MCP is an analytical support tool. It forces structured thinking about ethical compliance but does not provide legal certification or replace a qualified ethics review board.
What kind of data do I need for the validate_ai_ethics tool? +
You must supply detailed information covering all five axes: specific groups (stakeholders), quantifiable harm metrics, chosen bias attributes and metrics, decision factors, and a defined appeal process.
Does this MCP only work for lending models? +
No. The tool applies to any high-stakes AI system, including hiring tools, healthcare diagnostics, or content recommendation engines that impact individuals' lives.
If the audit fails, what does the result tell me? +
The failure report names the exact axis that failed and specifies the missing data point. For example, it might say 'HARMS_UNQUANTIFIED,' telling you precisely where to focus your remediation efforts.
How does validate_ai_ethics handle transparency? +
It demands more than just saying 'we are transparent.' It requires the system to explain decisions using plain language and provide counterfactual examples so users understand how changing inputs changes outcomes.
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