Permify MCP for AI. Manage every access policy without writing code.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
Permify manages complex access control policies and authorization models directly from your agent. Use this MCP to write, read, and test fine-grained schemas, define how users relate to resources, and perform real-time permission checks without touching a database.
Manage multi-tenant environments and audit relationships across entire systems.
What AI agents can do with Permify Automation
Bulk check permissions
Checks multiple permissions across several resources in one request.
Check permission
Performs a single, resource-based authorization check for immediate validation.
Create tenant
Sets up an entirely new, isolated client environment or tenant.
Run bulk checks to confirm if a group of users has access rights to multiple resources simultaneously.
Retrieve every subject linked to an entity, showing exactly who has established connections with that resource.
Write or update the underlying schemas and models that define your entire access control policy.
Find out which specific resources a given user can perform an action on, based on current policies.
Create and delete completely separate tenants, allowing you to test policies for different clients without interference.
Ask an AI about this
Waiting for input…
What AI agents can do with Permify: 22 Available Tools
These tools let you programmatically define, check, and modify every aspect of your application's complex authorization model.
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 Permify on VinkiusBulk Check Permissions
Checks multiple permissions across several resources in one request.
Check Permission
Performs a single, resource-based authorization check for immediate validation.
Create Tenant
Sets up an entirely new, isolated client environment or tenant.
Delete Bundle
Removes a pre-defined collection of policy rules or data bundles.
Delete Data
Clears out stored relationship tuples and associated attributes.
Delete Tenant
Permanently removes an existing tenant environment from the system.
Expand Permissions
Retrieves every subject that is related to a specified entity, mapping out connections.
List Schemas
Returns a list of all available schema versions for a specific tenant.
List Tenants
Retrieves a comprehensive list of every active tenant in the system.
Lookup Entity
Identifies which resources an individual user has permission to perform actions on.
Lookup Subject
Determines which subjects are capable of performing a specified action against a...
Partial Write Schema
Updates an authorization model schema by only modifying specific parts of the existing policy.
Read Attributes
Queries and retrieves stored, non-relational data attributes used in policies.
Read Bundle
Retrieves the detailed configuration and rules of a specific policy bundle.
Read Relationships
Queries and reads stored relational tuples, mapping connections between subjects and...
Read Schema
Inspects the full details of a specific schema version for review.
Run Bundle
Executes a predefined policy bundle to test its impact or generate results.
Subject Permission
Lists all specific permissions that a user is authorized to use on an entity.
Write Bundle
Defines or updates the ruleset for data bundles, creating new policy definitions.
Write Data
Creates and stores new relationship tuples or attribute data points.
Write Schema
Writes a brand-new authorization model schema from scratch for the system.
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 Permify 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 Permify, 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 Permify. 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 21 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Mapping out who has access to what is an absolute nightmare., Solved with Vinkius AI Gateway
Today, figuring out access rights involves jumping between the identity provider, the policy engine dashboard, and finally the database schema. You run a report, download it, open Excel, and then manually cross-reference user IDs against resource ownership tables. This takes hours of tedious copy-pasting just to answer one simple question: 'Can User X touch Resource Y?'
With this MCP, you ask your agent directly. The system handles the multi-step query across schemas, tuples, and bundles instantly. You get a definitive yes or no, along with the precise rule that made the decision. It’s about moving from manual data reconciliation to instant answers.
The Permify MCP gives you complete visibility into every relationship.
You don't have to rely on guesswork or audit logs that only show *what happened*, because the tool allows you to query the core policy data itself. You can use `read_relationships` to see all stored connections and then run `expand_permissions` to visualize every single subject linked to a key entity.
The difference is that instead of merely documenting access, you are actively querying the definitive state of your system's entire permission graph in real time.
What your AI can actually do with this
This connector lets you build and enforce robust authorization logic using natural language commands through your agent. Instead of manually writing SQL queries or navigating complex dashboard menus, you define the rules governing who can access what data.
Need to check if a user has permission to edit a specific document? You ask your agent, and it performs the necessary resource-based checks instantly. Want to see all the different ways users interact with your system? You run relationship audits to map every connection. The entire process of defining schemas, managing data tuples, and checking permissions happens in one conversational flow.
Because Vinkius hosts this MCP, you connect once from any compatible client—be it Claude, Cursor, or Windsurf—and get immediate access to the full suite of authorization tools needed for modern application security.
019ea5fe-1e94-7286-9298-8abaa1870b72 
Here's how it actually works
The bottom line is you use natural conversation to manage complex security policies instead of writing code or executing multiple API endpoints manually.
First, subscribe to the MCP and provide your Permify API URL and Secret Token credentials.
Next, prompt your agent with a natural language request, such as checking permissions or listing tenants.
Your agent executes the necessary tool calls against the MCP, returning an immediate, actionable result.
Who is this actually for?
Security Engineers who get bogged down in manual policy audits, and Backend Developers tired of constantly context-switching between the database and the application logic. This MCP lets you manage the entire IAM infrastructure from a single interface.
Auditing access control logic by running expand_permissions or verifying complex policy bundles without writing a single CLI command.
Quickly updating schemas and testing new relationship tuples using write_schema during rapid feature development cycles.
Automating the provisioning of client environments by creating, listing, or deleting tenants via dedicated API calls.
What Changes When You Connect
Stop relying on guesswork when auditing. Use expand_permissions to instantly see every single subject connected to a resource, giving you a complete relationship graph view.
Testing policies used to be slow. Now, use write_schema or partial_write_schema to define and test models rapidly, letting your agent handle the structure updates for you.
Forget manually querying data tables. The MCP lets you write and read complex relationship tuples using write_data and read_relationships, keeping all user-resource connections centralized.
When a client needs dedicated testing, use create_tenant. This allows your agent to provision an isolated environment, ensuring policy changes don't affect production systems.
Need quick validation? Run multiple checks at once with bulk_check_permissions instead of writing dozens of individual API calls for high-scale enterprise auditing.
See it in action
A client needs to verify if a new integration user has adequate rights.
Instead of asking the DBA to run a dozen permission checks, you ask your agent: 'Does user X have read and write access on document Y?' The agent executes check_permission and gives you an immediate ALLOWED or DENIED status.
A team needs to map out all historical data connections for compliance.
You prompt your agent to audit a specific resource, asking it to use expand_permissions. The agent returns every single subject that has ever interacted with the resource, solving the compliance mapping problem.
A developer needs to test a policy change for a new client.
You instruct your agent to first run create_tenant to build 'client-beta'. Then you use write_schema and write_data within that isolated tenant to simulate the full access model before deploying anything live.
The system needs to confirm if all policy rules are up to date after a major release.
You ask your agent to run list_schemas for the production environment, compare it against the last known good schema version using read_schema, and flag any discrepancies immediately.
The honest tradeoffs
Manually writing connection queries
Trying to map relationships by executing separate database calls for every subject-entity pair, which is slow and prone to human error.
Use the read_relationships tool. This lets your agent query stored relational tuples in one go, providing a complete view of connections without writing complex join queries.
Over-relying on simple user lists
Assuming that listing users is enough to confirm access, when the actual issue is defining what they can do with those resources.
Don't just list users. Use lookup_subject combined with subject_permission. This tells you exactly which actions a user is authorized for on specific entities.
Testing policies in the live environment
Making changes directly to production schemas because it's faster than setting up a test space, risking accidental outages.
Always use create_tenant first. Build and test your new policy rules using write_schema inside that dedicated, isolated client environment.
When It Fits, When It Doesn't
Use this MCP if your primary pain point is complexity in access control logic. If you spend time asking 'who can see what' or 'what can they do with it,' this tool is for you. It manages the relationship graph, not just user accounts. Don't use this if you simply need to store plain text data; those general database tools will suffice. Also, don't use this if your problem is identity management (user sign-ups/passwords)—it handles permissions, not authentication. If you only need a simple list of active users without policy depth, list_tenants or list_schemas are better starting points than trying to write a complex permission check.
Questions you might have
How do I check if two different users have conflicting permissions using Permify? +
You can use bulk_check_permissions. This tool lets you test multiple user access scenarios simultaneously, confirming any conflicts or overlaps in a single request.
Can the Permify MCP help me manage multiple clients? +
Yes. You use the create_tenant and delete_tenant tools to set up completely isolated environments for each client, keeping their policies separate and secure.
What is the difference between writing data and writing a schema with Permify? +
Writing data uses write_data to save specific relationship tuples (e.g., 'User A relates to Document B'). Writing a schema (write_schema) defines the rules that govern all possible relationships.
Do I need to run multiple checks every time I want to verify access? +
No. Instead of running many individual check_permission calls, you can use lookup_entity. This tool quickly shows what resources a user is authorized for based on the underlying policies.
Can Permify MCP help me view schema history? +
Yes. You first run list_schemas to get all version IDs, then use read_schema with a specific ID to inspect that model’s full details.
We've already built the connector for Permify. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 21 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.