Bring Authorization
to Pydantic AI
Learn how to connect OpenFGA (Fine-Grained Auth) to Pydantic AI and start using 16 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the OpenFGA (Fine-Grained Auth) MCP Server?
Connect your OpenFGA instance to any AI agent to manage Relationship-Based Access Control (ReBAC) through natural conversation. OpenFGA is an open-source fine-grained authorization solution inspired by Google's Zanzibar.
What you can do
- Store Management — Create, list, and delete isolated stores to manage authorization data for different environments or applications.
- Authorization Modeling — Define and retrieve complex authorization models using types and relations to represent your system's permissions.
- Tuple Management — Write, read, and track changes to relationship tuples that define which users have which relations to specific objects.
- Relationship Checks — Instantly evaluate whether a user has a specific relation to an object (e.g., 'can user:anne view document:1?').
- Health Monitoring — Quickly check the status of your OpenFGA instance to ensure high availability.
How it works
- Subscribe to this server
- Enter your OpenFGA API URL and API Token (if applicable)
- Start managing your authorization logic from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Security Engineers — Audit relationship tuples and verify authorization models without manual API calls.
- Backend Developers — Quickly test and iterate on authorization models during development directly from the IDE.
- DevOps & SREs — Monitor store health and manage authorization environments across different clusters.
Built-in capabilities (16)
Perform multiple checks in one request
Check if a user has a relation to an object
Create a new OpenFGA store
Delete an OpenFGA store
Expand a relation into a tree
Get a specific authorization model
Get OpenFGA store details
Check OpenFGA server health
List authorization models
List all objects a user can access
List all OpenFGA stores
List all users who have a relation to an object
Read changes to relationship tuples
Query stored relationship tuples
Write a new authorization model
Add or delete relationship tuples
Why Pydantic AI?
Pydantic AI validates every OpenFGA (Fine-Grained Auth) tool response against typed schemas, catching data inconsistencies at build time. Connect 16 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
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your OpenFGA (Fine-Grained Auth) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your OpenFGA (Fine-Grained Auth) connection logic from agent behavior for testable, maintainable code
OpenFGA (Fine-Grained Auth) in Pydantic AI
OpenFGA (Fine-Grained Auth) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect OpenFGA (Fine-Grained Auth) 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 OpenFGA (Fine-Grained Auth) in Pydantic AI
The OpenFGA (Fine-Grained Auth) 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 16 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
OpenFGA (Fine-Grained Auth) for Pydantic AI
Every tool call from Pydantic AI to the OpenFGA (Fine-Grained Auth) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I check if a specific user has access to a resource?
You can use the check_relation tool. Provide the store ID and the relationship details (user, relation, and object) to get an immediate boolean response on whether the access is permitted.
Can I see the history of changes made to relationship tuples?
Yes, the read_changes tool allows you to retrieve the changelog of relationship tuples for a specific store, optionally filtered by object type.
How do I define a new authorization model?
Use the write_authorization_model tool. You will need to provide the store ID, the schema version, and a JSON array of type definitions that describe your relations.
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 OpenFGA (Fine-Grained Auth) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai
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