Skip to main content
Glama

AI Code Toolkit

by AgiFlow
ScaffoldFeaturePrompt.ts1.6 kB
import scaffoldFeatureTemplate from '../instructions/prompts/scaffold-feature.md?raw'; import { TemplateService } from '../services/TemplateService'; import type { PromptDefinition, PromptMessage } from './types'; /** * Prompt for scaffolding a new feature in an existing project */ export class ScaffoldFeaturePrompt { static readonly PROMPT_NAME = 'scaffold-feature'; private templateService = new TemplateService(); constructor(private isMonolith = false) {} /** * Get the prompt definition for MCP */ getDefinition(): PromptDefinition { return { name: ScaffoldFeaturePrompt.PROMPT_NAME, description: 'Scaffold a new feature (page, component, service, etc.) in an existing project', arguments: [ { name: 'request', description: 'Describe the feature you want to add (optional)', required: false, }, { name: 'projectPath', description: 'Path to the project (e.g., "apps/my-app") - optional if can be inferred', required: false, }, ], }; } /** * Get the prompt messages */ getMessages(args?: { request?: string; projectPath?: string }): PromptMessage[] { const request = args?.request || ''; const projectPath = args?.projectPath || ''; const text = this.templateService.renderString(scaffoldFeatureTemplate, { request, projectPath, isMonolith: this.isMonolith, }); return [ { role: 'user', content: { type: 'text', text, }, }, ]; } }

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AgiFlow/aicode-toolkit'

If you have feedback or need assistance with the MCP directory API, please join our Discord server