Supports the architecting and development of FastAPI applications, including scaffolding project structures, planning API routes, and generating endpoint code.
Facilitates building backends using Supabase for the database layer, specifically through planning and generating SQLModel schemas and relationships.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Backend Architect MCP Serverinitialize a new FastAPI project and tell me what to build next"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Backend Architect MCP Server
An expert MCP toolchain designed to act as a Backend Architect for AI agents. This server enforces a strict "Atomic Development" workflow for building Python FastAPI + Supabase backends.
🚀 Overview
The Backend Architect server guides an agent through a Plan -> Prompt -> Write loop, ensuring that database models, API routes, and tests are built in the correct dependency order.
Key Features
Atomic Development: Focuses on one component at a time.
Workflow Enforcement: Models → Routes → Tests (respects model dependencies).
Auto-Imports: Automatically updates
__init__.pyfiles for models and routes.State Persistence: Maintains
.mcp_state.jsonto track building progress.Contextual Prompts: Generates specialized system prompts for each component.
🛠️ Tech Stack
Python 3.12
MCP SDK (FastMCP)
UV (Dependency Manager)
Pydantic (State Validation)
📦 Installation
Ensure you have uv installed. Then, clone the repository and install dependencies:
🛠️ Tools Reference
1. Initialization
initialize_project(root_path: str = "."): Scaffolds the FastAPI project structure andpyproject.toml. Defaults to the current working directory.
2. Planning
save_roles_plan(roles: list): Define user roles and permissions.save_database_plan(models: list): Define SQLModel schemas and relationships.save_route_plan(routes: list): Define API endpoints and methods.save_test_plan(tests: list): Define simulation scenarios.
3. Execution
get_next_pending_task(): The "Traffic Cop" that tells you exactly what to build next.get_file_instruction(task_type: str, task_name: str): Returns a strict system prompt for the AI to follow.write_component_file(type: str, name: str, content: str): Writes the code and marks the task as "done".
🔄 The Loop
Initialize: Set up your project root.
Plan: Feed the architect your schemas and endpoints.
Draft: Ask
get_next_pending_task()for the current objective.Learn: Get instructions via
get_file_instruction().Write: Submit code via
write_component_file().Repeat: Until the entire backend is architected.
⚙️ MCP Configuration
Add this to your MCP settings file (e.g., mcp_config.json or your IDE's MCP settings):
Use the absolute path to the directory where you cloned this repository for the--project argument. This ensures the server can find its dependencies regardless of where your AI agent is currently working.
Built with ❤️ for the AI-First Developer.