Compatible with every major AI agent and IDE
What is the Cerbos (Access Control) MCP Server?
Connect your Cerbos instance to any AI agent to streamline authorization management and policy auditing through natural language.
What you can do
- Permission Checks — Use
check_resourcesto evaluate if a principal (user) has the rights to perform specific actions on resources. - Query Planning — Generate AST query plans with
plan_resourcesto filter database results based on complex authorization logic. - Policy Management — List, retrieve, add, or delete policies (RBAC/ABAC) using the Admin API tools like
list_policiesandadd_policy. - Schema & Auditing — Inspect resource schemas and review access logs with
list_auditLogsto ensure compliance. - Health & Metrics — Monitor your PDP (Policy Decision Point) status with
get_healthandget_metricsdirectly from the chat.
How it works
- Subscribe to this server
- Enter your Cerbos PDP URL and Admin credentials (if required for policy management)
- Start auditing and managing your access control logic from Claude, Cursor, or any MCP client
Who is this for?
- Security Engineers — quickly audit existing policies and verify permission logic without manual API calls
- Backend Developers — test authorization scenarios and generate database filter plans during development
- Compliance Officers — retrieve audit logs and policy definitions to ensure organizational security standards
Built-in capabilities (19)
Add a new policy
Add or update a schema
Perform a single AuthZEN access evaluation
Perform batch AuthZEN access evaluations
Check permissions for a set of resources
Delete a policy by ID
Disable a policy
Enable a policy
Get AuthZEN configuration metadata
Get Cerbos health status
Get Prometheus metrics from Cerbos
Get a specific policy by ID
Get a specific schema by ID
Get Cerbos server version and build information
List audit logs
List all policies
List all schemas
Produce a query plan (AST) for filtering resources
Update an existing policy
Why Pydantic AI?
Pydantic AI validates every Cerbos (Access Control) tool response against typed schemas, catching data inconsistencies at build time. Connect 19 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
- —
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Cerbos (Access Control) integration code
- —
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
- —
Dependency injection system cleanly separates your Cerbos (Access Control) connection logic from agent behavior for testable, maintainable code
Cerbos (Access Control) in Pydantic AI
Cerbos (Access Control) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Cerbos (Access Control) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Cerbos (Access Control) in Pydantic AI
The Cerbos (Access Control) MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 19 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Cerbos (Access Control) for Pydantic AI
Every tool call from Pydantic AI to the Cerbos (Access Control) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I test if a specific user has access to a resource without writing code?
Yes. You can ask the agent to use the check_resources tool by providing the principal (user) details and the resource you want to check. The agent will return the allowed or denied status based on your Cerbos policies.
How do I view all the authorization policies currently loaded in my Cerbos server?
Simply ask the agent to 'list all policies'. It will invoke the list_policies tool (requires Admin credentials) and display the IDs of all active policies in your environment.
Can the AI help me generate filters for my database based on permissions?
Yes, by using the plan_resources tool. The agent will generate a query plan (AST) that describes the conditions under which a user is allowed to access resources, which you can then apply to your database queries.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your Cerbos (Access Control) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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