FastAPI Scaffolder MCP Server
Generates production-ready FastAPI applications from declarative YAML architecture specifications, including typed models, routers, configuration, dependency wiring, and test suites.
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., "@FastAPI Scaffolder MCP ServerBuild a FastAPI app from the YAML spec in ./architecture.yaml"
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.
FastAPI Scaffolder
FastAPI Scaffolder is a developer tool and AI system agent that transforms human- and machine-readable YAML architecture specifications into production-ready FastAPI applications.
It provides both a CLI interface for humans and a Model Context Protocol (MCP) server for AI agents (Gemini, Claude Desktop, Cursor, Windsurf, LangChain) to generate modular APIs instantly.
Capabilities
Dual Interface: Native CLI for developers and a zero-dependency JSON-RPC 2.0 MCP server for AI models.
Declarative Schemas: Define microservices, endpoints, HTTP methods, Pydantic request/response payloads, and service dependencies in single or split YAML files.
Modular Generation: Renders complete project structures complete with typed Pydantic models, FastAPI routers, configuration, dependency wiring, and test suites.
Python 3.14 Ready: Built natively without third-party wrapper bottlenecks or SDK version locks.
Related MCP server: MCP Server Template
Installation
Prerequisites
Python
>= 3.10(Tested up to3.14)uvorpip
Install Locally (Editable Mode)
# Clone and enter directory
cd fastapi-scaffolder
# Install with CLI and MCP entry points
pip install -e .
This registers two global commands in your active virtual environment:
scaffold— Human-facing CLI toolscaffold-mcp— Executable stdio MCP server for AI clients
Schema Specification (architecture.yaml)
Define your API architecture using standard YAML:
system_name: PaymentInvoicingPlatform
version: 1.0.0
services:
- name: AuthService
description: Handles user authentication and tokens
dependencies: []
endpoints:
- path: /auth/login
method: POST
summary: Authenticate user
request_body:
- name: email
type: str
- name: password
type: str
response_body:
- name: access_token
type: str
- name: InvoiceService
description: Manages client invoices
dependencies:
- AuthService
endpoints:
- path: /invoices
method: POST
summary: Create client invoice
request_body:
- name: client_email
type: str
- name: amount
type: float
response_body:
- name: invoice_id
type: str
- name: status
type: str
Usage Guide
1. Human CLI Usage
Scaffold an app directly from the terminal using the scaffold command:
# Generate app from YAML spec
scaffold -i architecture.yaml -o ./my_fastapi_app
# Combine multiple service specs
scaffold -i auth_service.yaml billing_service.yaml -o ./monorepo_app
2. AI Setup with Model Context Protocol (MCP)
scaffold-mcp communicates over Standard Input/Output (stdio) via JSON-RPC 2.0.
Cursor / Claude Desktop / Windsurf Setup
Add fastapi-scaffolder to your MCP configuration file (claude_desktop_config.json or Cursor MCP settings):
{
"mcpServers": {
"fastapi-scaffolder": {
"command": "/path/to/your/venv/bin/scaffold-mcp",
"args": []
}
}
}
Replace /path/to/your/venv/bin/scaffold-mcp with the absolute path returned by which scaffold-mcp.
System Prompt Directive for AI Agents
Add this instruction to your LLM system prompt so it outputs compliant YAML to invoke the tool:
FastAPI Scaffolder Schema Directive:
When generating an API, output a YAML string structured as follows:
system_name: MySystem version: 1.0.0 services: - name: ServiceName description: Summary of responsibility dependencies: [] endpoints: - path: /items/{id} method: GET summary: retrieve item response_body: - name: id type: strPass this raw YAML string to the
build_fastapi_apptool withoutput_directory.
3. Usage with Google Gemini SDK
To run fastapi-scaffolder inside Gemini agent workflows:
from google import genai
from google.genai import types
from scaffolder.mcp_server import execute_scaffold
client = genai.Client()
# Pass the tool execution function to Gemini
response = client.models.generate_content(
model="gemini-2.5-flash",
contents="Design a user profile microservice in YAML and build the code in ./user_service",
config=types.GenerateContentConfig(
tools=[execute_scaffold]
)
)
# Execute the returned function call
if response.function_calls:
for call in response.function_calls:
if call.name == "execute_scaffold":
result = execute_scaffold(call.args)
print(result)
Testing the MCP Server Manually
Verify that the MCP server starts and receives messages via stdio:
# Run server executable
scaffold-mcp
Paste this test payload into stdout and press Enter:
{"jsonrpc": "2.0", "id": 1, "method": "tools/list"}
Expected Response:
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"tools": [
{
"name": "build_fastapi_app",
"description": "Scaffolds a complete FastAPI codebase from a YAML architecture definition.",
"inputSchema": {
"type": "object",
"properties": {
"yaml_spec": {
"type": "string",
"description": "Raw YAML string matching the SystemArchitecture schema."
},
"output_directory": {
"type": "string",
"description": "Output directory path for generated files.",
"default": "./generated_app"
}
},
"required": ["yaml_spec"]
}
}
]
}
}
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
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