Scaffold Generator▌
by agiflow
Quickly rp prototype web apps with Scaffold Generator: create consistent scaffolding using templates, variable substitut
Generates code scaffolding for modern web applications using template-based boilerplate creation and feature addition with variable substitution, conditional file inclusion, and schema validation for rapid prototyping and consistent development patterns.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Frontend developers starting new projects
- / Teams enforcing consistent code structure
- / Rapid prototyping and MVP development
- / Standardizing development workflows
capabilities
- / Generate project scaffolds from templates
- / Add features to existing codebases
- / Substitute variables in template files
- / Validate code structure against schemas
- / Create consistent development patterns
- / Configure MCP server setups automatically
what it does
Generates code scaffolding and boilerplate for modern web applications using customizable templates with variable substitution and schema validation.
about
Scaffold Generator is a community-built MCP server published by agiflow that provides AI assistants with tools and capabilities via the Model Context Protocol. Quickly rp prototype web apps with Scaffold Generator: create consistent scaffolding using templates, variable substitut It is categorized under developer tools.
how to install
You can install Scaffold Generator in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
AGPL-3.0
Scaffold Generator is released under the AGPL-3.0 license.
readme
AI Code Toolkit
This repo provides:
- project and feature scaffolding via templates
- file-level design guidance before edits
- rule-based review after edits
- design-system discovery for frontend work
Quick Start
Requirements:
- Node.js >= 18
- an MCP-compatible agent such as Claude Code, Cursor, or Gemini CLI
1. Initialize a Workspace
# Existing project
npx @agiflowai/aicode-toolkit init
# New project
npx @agiflowai/aicode-toolkit init --name my-app --project-type monolith
This creates templates/ and .toolkit/settings.yaml. Projects reference templates through sourceTemplate in project.json or .toolkit/settings.yaml.
2. Configure MCP
init can configure MCP automatically. For manual setup, add the servers you need to your agent config.
Example for Claude Code:
{
"mcpServers": {
"scaffold-mcp": {
"command": "npx",
"args": ["-y", "@agiflowai/scaffold-mcp", "mcp-serve", "--admin-enable"]
},
"architect-mcp": {
"command": "npx",
"args": [
"-y", "@agiflowai/architect-mcp", "mcp-serve",
"--admin-enable",
"--design-pattern-tool", "codex",
"--review-tool", "gemini-cli"
]
},
"style-system": {
"command": "npx",
"args": ["-y", "@agiflowai/style-system", "mcp-serve"]
}
}
}
Useful flags:
--admin-enable: enable admin/template-authoring tools--design-pattern-tool <tool>: use an LLM to filter design patterns--review-tool <tool>: use an LLM for review output
3. Verify
Ask the agent:
What boilerplates are available?
It should call list-boilerplates. If not, restart the agent.
Repo Layout
AI agent
├─ scaffold-mcp
├─ architect-mcp
├─ style-system
└─ one-mcp
↓
templates/
├─ scaffold.yaml
├─ architect.yaml
└─ RULES.yaml
scaffold-mcp
Generates projects and feature boilerplate from templates.
Core tools:
list-boilerplatesuse-boilerplatelist-scaffolding-methodsuse-scaffold-method
Admin tools:
generate-boilerplategenerate-feature-scaffoldgenerate-boilerplate-file
architect-mcp
Provides file-specific patterns before edits and reviews changes against RULES.yaml.
Core tools:
get-file-design-patternreview-code-change
Admin tools:
add-design-patternadd-rule
style-system
Provides theme, CSS class, and component discovery tools.
Core tools:
list_themesget_css_classesget_component_visuallist_shared_componentslist_app_components
one-mcp
Provides progressive tool discovery to reduce MCP prompt overhead.
Typical Workflow
Create a Project
User: "Create a Next.js app called dashboard"
Agent:
1. list-boilerplates
2. use-boilerplate
3. Project is generated
Add a Feature
User: "Add a products API route"
Agent:
1. list-scaffolding-methods
2. use-scaffold-method
3. Feature files are generated
Edit a File Safely
User: "Add a products page"
Agent:
1. get-file-design-pattern
2. edit the file using the returned patterns and rules
3. review-code-change
4. fix any violations
Style a Component
User: "Style the button with our theme colors"
Agent:
1. get_css_classes
2. list_shared_components
3. update the component
4. get_component_visual
Template Structure
templates/
└── nextjs-15/
├── scaffold.yaml
├── architect.yaml
├── RULES.yaml
└── boilerplate/
scaffold.yaml
Defines boilerplates and feature scaffolds.
boilerplates:
- name: nextjs-15-app
description: "Next.js 15 with App Router"
targetFolder: apps
includes:
- boilerplate/**/*
features:
- name: add-route
description: "Add route with page and layout"
variables_schema:
name: { type: string, required: true }
includes:
- features/route/**/*
architect.yaml
Defines file-level patterns that should be shown before edits.
patterns:
- name: server-component
description: "Default for page components"
file_patterns:
- "**/app/**/page.tsx"
description: |
- Use async/await for data fetching
- Keep components focused on rendering
- Move business logic to server actions
RULES.yaml
Defines review rules. Rules can be inherited from a global templates/RULES.yaml.
version: '1.0'
template: typescript-lib
rules:
- pattern: src/services/**/*.ts
description: Service Layer Implementation Standards
must_do:
- rule: Create class-based services with single responsibility
codeExample: |-
export class DataProcessorService {
async processData(input: string): Promise<ProcessedData> {
// Implementation
}
}
- rule: Use dependency injection for composability
must_not_do:
- rule: Create static-only utility classes - use functions
codeExample: |-
// ❌ BAD
export class Utils {
static format(s: string) {}
}
// ✅ GOOD
export function format(s: string): string {}
Project Types
Monorepo
Each project references its template in project.json.
my-workspace/
├── apps/
│ └── web-app/
│ └── project.json
├── packages/
│ └── shared-lib/
│ └── project.json
└── templates/
Monolith
Monoliths use .toolkit/settings.yaml.
version: "1.0"
projectType: monolith
sourceTemplate: nextjs-15
Built-in Templates
Included templates:
| Template | Stack | Includes |
|---|---|---|
nextjs-drizzle | Next.js 15, App Router | TypeScript, Tailwind 4, Drizzle, Storybook |
typescript-lib | TypeScript Library | ESM/CJS, Vitest, TSDoc |
typescript-mcp-package | MCP Server | Commander, MCP SDK |
Custom Templates
For template authoring, start from an existing repo or template and use the admin prompts:
/generate-boilerplate
/generate-feature-scaffold
For design/rule authoring, use:
add-design-patternadd-rule
Supported Agents
| Agent | Config Location | Status |
|---|---|---|
| Claude Code | .mcp.json | Supported |
| Cursor | .cursor/mcp.json | Supported |
| Gemini CLI | .gemini/settings.json | Supported |
| Codex CLI | .codex/config.json | Supported |
| GitHub Copilot | VS Code settings | Supported |
| Windsurf | - | Planned |
Packages
| Package | Description |
|---|---|
| @agiflowai/aicode-toolkit | CLI for init and config sync |
| @agiflowai/scaffold-mcp | Scaffolding server |
| @agiflowai/architect-mcp | Pattern and review server |
| @agiflowai/style-system | Design-system server |
| @agiflowai/one-mcp | MCP proxy for progressive discovery |
Contributing
See CONTRIBUTING.md.
License
FAQ
- What is the Scaffold Generator MCP server?
- Scaffold Generator is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Scaffold Generator?
- This profile displays 56 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
List & Promote Your MCP Server
Share your MCP server with the developer community
Ratings
4.7★★★★★56 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
Scaffold Generator is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Hana White· Dec 20, 2024
We wired Scaffold Generator into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Noor Abebe· Dec 16, 2024
Scaffold Generator is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sofia Jackson· Dec 16, 2024
Useful MCP listing: Scaffold Generator is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Rahul Santra· Nov 15, 2024
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Amelia Mensah· Nov 11, 2024
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Harper Brown· Nov 7, 2024
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Zaid Chawla· Oct 26, 2024
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Pratham Ware· Oct 6, 2024
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Amelia Kim· Oct 2, 2024
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
showing 1-10 of 56