Cerbos MCP for AI Agents. Manage complex access control policies and audit permissions in real time
Cerbos provides an open standard connection for managing access control policies and checking permissions using your AI agent. It lets developers verify exactly if a user can perform a specific action on a resource, keeping authorization logic outside of application code. You'll audit complex security rules, manage policies (RBAC/ABAC), and generate query plans directly through natural language prompts.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Verify if a specific user or group is authorized to perform an action on a given data object.
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What AI agents can do with 20 Tools in the Cerbos (Access Control) MCP for Policy Management
Use these tools to manage every aspect of your application's security layer—from creating policies to auditing historical access logs.
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 Cerbos (Access Control) MCPAdd Policy
Creates a completely new access control rule for the system.
Add Schema
Adds or updates the structural definition (schema) of a resource type.
Authzen Evaluation
Performs a single, immediate test to determine if access is allowed using AuthZEN...
Authzen Evaluations
Runs multiple, batch checks to evaluate permissions across several scenarios at once.
Check Resources
Checks if a set of users have the required rights for specific resources.
Delete Policy
Removes an existing policy entirely using its unique identifier.
Disable Policy
Temporarily turns off a policy without deleting it, useful for testing or maintenance.
Enable Policy
Restores a previously disabled access control policy to active status.
Get Authzen Config
Retrieves metadata about the AuthZEN configuration setup.
Get Health
Returns the current operational status of the Cerbos policy engine.
Get Metrics
Pulls performance metrics data, useful for monitoring system load and usage patterns.
Get Policy
Retrieves the full text of a specific policy by its ID.
Get Schema
Fetches the detailed structure definition for a schema type.
Get Server Info
Gets version and build information about the Cerbos server instance.
List Audit Logs
Generates a list of all recorded access control audit logs for review.
List Policies
Displays every single policy currently active in the system.
List Schemas
Lists all defined resource schemas available to the application.
Plan Resources
Creates a database query plan (AST) that filters resources according to complex authorization logic.
Update Policy
Modifies the rules and parameters of an existing policy.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
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.
Start a conversation
Open a new chat. The Cerbos MCP for AI Agents 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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Cerbos (Access Control), then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cerbos. 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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Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Cerbos (Access Control) MCP: Policy Management and Auditing
Today, managing access control means developers spend cycles writing boilerplate code that checks user roles against resource types. When a policy needs to change—say, moving from role-based access to attribute-based access—the team has to manually hunt down every instance of the old logic across dozens of files and redeploy everything just for a rule tweak.
With this MCP, you simply describe the required changes in plain English. Your agent interacts with the system using tools like `add_policy` or `update_policy`. You get immediate confirmation that the policy was correctly registered and activated at the engine level, drastically reducing deployment risk.
Cerbos (Access Control) MCP: Testing Authorization in Development
The manual development process involves setting up mock users, writing unit tests for every single access path, and then running those tests to ensure the correct policies are hit. This is time-consuming, brittle, and often misses edge cases involving overlapping permissions.
Now, you can ask your agent to generate a query plan using `plan_resources` based on complex policy logic. You get an optimized, data-filtering blueprint before writing any database interaction code. It's testing the rule itself, not just the endpoint.
What Cerbos MCP for AI Agents MCP does for your AI
Managing permissions used to mean writing messy, complex logic into your main codebase—logic that breaks when requirements change or needs auditing. This MCP changes that process entirely. It lets you connect your AI agent to a dedicated policy engine, treating authorization rules like data instead of code. You can now ask questions like, 'Can this user delete this report?' and get an immediate, definitive answer based on your current security policies.
Furthermore, if you need to check the health of the entire system or audit past access attempts, your agent handles it without needing manual API calls. By using Vinkius as your central catalog, you connect once from any compatible client and gain control over all these policy functions through conversation.
019e3875-7d51-7276-a490-038c89aa5d52 
How to set up Cerbos MCP for AI Agents MCP
The bottom line is that you get an immediate, conversation-based interface for managing and testing core security rules without writing any code.
Subscribe to this MCP on Vinkius.
Provide your Cerbos Policy Decision Point URL and necessary administrative credentials to your AI client.
Ask your agent a natural language question—like, 'Show me all policies related to the expense report resource.'—and it handles the rest.
Who uses Cerbos MCP for AI Agents MCP
Security Engineers and Backend Developers need this. If your job involves figuring out why a user suddenly lost access to a feature, or if you spend time manually checking policy definitions in an admin console, this MCP saves you hours of clicking through dashboards.
Auditing existing policies and verifying permission logic for compliance checks without running manual scripts.
Testing complex authorization scenarios early in the development cycle, especially generating database filter plans before writing service code.
Retrieving detailed access audit logs and verifying that policy definitions meet specific organizational security standards.
Benefits of connecting Cerbos MCP for AI Agents MCP
You stop manually running API calls. Your agent handles policy definitions, allowing you to quickly use list_policies or update_policy by simply asking a natural language question.
Audit risk instantly. Need to know if an action was allowed? Running the check_resources tool lets you test permissions for any user against any resource without touching your code base.
Improve developer speed. Instead of guessing how database filters work, use plan_resources to generate and test optimized query plans based on policy rules directly within your agent chat.
Ensure compliance easily. By using the audit logging tools like list_audit_logs, you can retrieve comprehensive records proving that security standards were followed.
Gain visibility into system health. You'll get immediate answers about performance or status by running get_health or pulling operational metrics with get_metrics.
Cerbos MCP for AI Agents MCP use cases
Verifying a new feature rollout
A backend developer needs to ensure that only managers can approve expenses. They prompt their agent: 'Check if the manager role can execute the approval action on expense reports.' The agent uses check_resources and confirms the exact required permissions before the code is merged.
Investigating a data breach
A security engineer discovers unusual access patterns. They prompt their agent to 'List all audit logs for resource type X in the last week.' The agent uses list_audit_logs and provides a chronological list, immediately narrowing down the scope of the investigation.
Refactoring authorization logic
A developer needs to change how department ownership works. They ask their agent to 'Update policy resource.dept.v1 to include owner attribute.' The agent uses update_policy and confirms the successful modification.
Optimizing reporting queries
The team needs a report that only shows records owned by the current user's department. Instead of writing complex SQL, they prompt their agent to 'Generate an AST query plan for filtering resources based on department ownership.' The agent uses plan_resources.
Cerbos MCP for AI Agents MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Hardcoding permission checks
Writing if user.role == 'admin' and resource.status == 'active': allow() directly into the application service layer. This means every time a rule changes, a developer must redeploy code.
Instead of hardcoding logic, use your agent to run check_resources. You ask your agent: 'Can this user delete this record?' The policy engine handles the decision without you touching the source code.
Ignoring existing policies
Building a new feature and forgetting that an old, undocumented policy might interfere with the new functionality. You'll run into unpredictable bugs in production.
Always start by running list_policies to see everything active. Then use your agent to check for conflicts or gaps before you proceed.
Assuming policy structure
Manually trying to write a complex new rule without knowing the exact schema definition, leading to invalid policies that break at runtime.
First, run list_schemas and then use get_schema on specific resources. This ensures you have the correct data structure before attempting to add or modify any policy.
When to use Cerbos MCP for AI Agents MCP
Use this MCP if your core problem is determining authorization—that is, answering the question 'Does X have permission Y on Z?' You need a way to test, audit, and manage complex security rules (RBAC/ABAC) outside of your primary application code. Don't use this if you just need simple data retrieval or CRUD operations; those belong in standard database connectors. If all you need is to know the current health status, while get_health works, consider a simpler monitoring tool. When in doubt about policy complexity, always check with list_policies first.
Frequently asked questions about Cerbos MCP for AI Agents MCP
How does the Cerbos MCP help manage my company's access control policies? +
You can use this MCP to centralize policy management. Instead of coding rules into your application, you define them using tools like list_policies and add_policy. Your agent then handles all checking and enforcement logic for you.
Can I audit past security events with the Cerbos MCP? +
Yes. You can use this MCP to retrieve comprehensive records of every access decision using tools like list_audit_logs. This lets compliance teams prove exactly who accessed what and when.
Does this MCP help developers write more secure code? +
Absolutely. You can use it during development to test authorization scenarios by generating query plans with plan_resources, ensuring your database queries are always correctly scoped based on security rules before they ever reach production.
What if I need to update a policy, but I'm not sure which one is correct? +
You can list all available policies using list_policies and then use the agent to retrieve specific details about any rule with get_policy. This gives you the full context needed for safe updates.
How do I check if a user has permission without writing boilerplate code? +
You simply ask your AI agent to check permissions using tools like check_resources. The MCP handles all the complex policy evaluation, returning a simple ALLOWED or DENIED status for you.