MCP server
by agiflow
Quickly rp prototype web apps with Scaffold Generator: create consistent scaffolding using templates, variable substitut
Generates code scaffolding and boilerplate for modern web applications using customizable templates with variable substitution and schema validation.
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.
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.
AGPL-3.0
Scaffold Generator is released under the AGPL-3.0 license.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
Scaffold Generator is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We wired Scaffold Generator into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Scaffold Generator is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Scaffold Generator is the kind of server we cite when onboarding engineers to host + tool permissions.
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
We evaluated Scaffold Generator against two servers with overlapping tools; this profile had the clearer scope statement.
Scaffold Generator is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
showing 1-10 of 56
This repo provides:
Requirements:
# 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.
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 outputAsk the agent:
What boilerplates are available?
It should call list-boilerplates. If not, restart the agent.
AI agent
├─ scaffold-mcp
├─ architect-mcp
├─ style-system
└─ one-mcp
↓
templates/
├─ scaffold.yaml
├─ architect.yaml
└─ RULES.yaml
Generates projects and feature boilerplate from templates.
Core tools:
list-boilerplatesuse-boilerplatelist-scaffolding-methodsuse-scaffold-methodAdmin tools:
generate-boilerplategenerate-feature-scaffoldgenerate-boilerplate-fileProvides file-specific patterns before edits and reviews changes against RULES.yaml.
Core tools:
get-file-design-patternreview-code-changeAdmin tools:
add-design-patternadd-ruleProvides theme, CSS class, and component discovery tools.
Core tools:
list_themesget_css_classesget_component_visuallist_shared_componentslist_app_componentsProvides progressive tool discovery to reduce MCP prompt overhead.
User: "Create a Next.js app called dashboard"
Agent:
1. list-boilerplates
2. use-boilerplate
3. Project is generated
User: "Add a products API route"
Agent:
1. list-scaffolding-methods
2. use-scaffold-method
3. Feature files are generated
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
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
templates/
└── nextjs-15/
├── scaffold.yaml
├── architect.yaml
├── RULES.yaml
└── boilerplate/
scaffold.yamlDefines 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.yamlDefines 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.yamlDefines 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 {}
Each project references its template in project.json.
my-workspace/
├── apps/
│ └── web-app/
│ └── project.json
├── packages/
│ └── shared-lib/
│ └── project.json
└── templates/
Monoliths use .toolkit/settings.yaml.
version: "1.0"
projectType: monolith
sourceTemplate: nextjs-15
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 |
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| 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 |
| 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 |
See CONTRIBUTING.md.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.