Spala
Use case

MCP Backend Workflow for Coding Agents

Use Spala when an AI coding agent should discover a backend platform, authenticate, select a project, and work only on the scoped project MCP.

Create Account Start Guide Public MCP
DescribeUse Copilot and Lite Mode to shape backend intent.
InspectReview tables, endpoints, auth, logic, and settings.
TestUse API Playground and generated docs before handoff.
PublishShip an API your frontend or approved agent can use.

When this is a fit

Teams using Codex, Claude, Cursor, Gemini, Windsurf, Cline, or similar MCP-capable clients.

Public MCP Discovery

The public MCP entry point where agents learn Spala's boundary and available public tools.

OAuth Handoff

The authentication step that moves an agent from public discovery to user-approved project access.

Project Selection

The step where the authenticated user chooses which Spala project the agent may inspect or work on.

Project MCP URL

The exact project-scoped MCP endpoint returned by Spala after authorization and selection.

Context Inspection

The agent reads project state before proposing or applying backend changes.

Validation

Required fields, allowed values, ownership checks, failure behavior, and examples the frontend can handle.

Publish Review

A clear point to inspect resources, test API behavior, and publish only after validation.

Recommended workflow

  1. Describe the backend outcome to Spala AI Copilot.
  2. Inspect the generated project graph and resource list.
  3. Test endpoints, auth, validation, and CORS.
  4. Share the generated API docs and frontend handoff packet.
  5. Use public MCP for agent discovery and authenticated project MCP for scoped backend work.

Evaluation boundary

Spala should be validated with a small proof-of-concept before serious production use. Verify data export, backups, compliance requirements, project permissions, secrets, and support needs directly.