OpenFGA MCP Server

The OpenFGA MCP Server bridges the gap between fine‑grained authorization engines and AI assistants that speak the Model Context Protocol. By exposing OpenFGA’s powerful policy engine through MCP, developers can let AI agents query, design, and even modify authorization models without leaving their preferred workflow tools. This is especially valuable for teams that want to automate security reviews, generate SDKs, or surface policy insights directly inside IDEs and code editors.

At its core, the server implements a lightweight MCP interface that translates AI requests into OpenFGA API calls. When an agent asks “What objects does user Alice own?”, the server forwards that query to OpenFGA, retrieves the result set, and streams it back via MCP. The same mechanism works in reverse: an AI can suggest a new relationship or a policy change, which the server forwards as a write request to OpenFGA (writes are disabled by default for safety). This bidirectional flow lets developers prototype policies, validate them against live data, and iterate quickly—all within the same conversational context that AI assistants provide.

Key capabilities include:

• Model design assistance – The server can expose OpenFGA’s schema definition language, allowing agents to recommend best‑practice patterns or refactor existing models.
• Code generation – By providing full context on the current model, AI tools can produce SDK snippets and documentation that are guaranteed to match the live policy state.
• Instance management – Agents can query store statistics, list objects or relationships, and even trigger bulk updates when operating in writeable mode.
• Transport flexibility – Support for both and streamable HTTP transports means the server can run locally in a Docker container or be exposed as a network service.
• Stateless operation – When is enabled, clients that do not maintain session state can still leverage the server without extra overhead.

Typical real‑world scenarios include:

• Security engineering pipelines – An AI assistant reviews new policy drafts, simulates their impact on existing data, and suggests optimizations before a merge.
• Developer onboarding – New team members can ask an AI to walk through the authorization model, receive live examples, and automatically generate client code snippets.
• Operational monitoring – Agents monitor policy drift or unexpected permission changes by querying OpenFGA on a schedule and alerting stakeholders.
• Cross‑tool integration – IDEs like Cursor, Zed, or Claude Desktop can embed the MCP client to provide inline policy insights while coding.

By integrating seamlessly with any MCP‑compliant AI workflow, the OpenFGA MCP Server empowers teams to treat authorization as a first‑class citizen in their development lifecycle, reducing friction between policy definition and practical usage while keeping security at the forefront.