Pre-built prompts that chain multiple MCP tools into complete workflows. Create content briefs, generate articles, or run full page SEO improvements—your AI handles the orchestration.
Analyze a Nuxt page and generate SEO improvements. Orchestrates multiple tools in sequence.
| Parameter | Type | Description |
|---|---|---|
filePath | string | Path to Vue file (e.g., app/pages/about.vue) |
fileContent | string | Content of the Vue file |
topic | string | Page topic for keyword research (optional, auto-detected) |
nuxtConfig | string | nuxt.config.ts content (optional, for module detection) |
liveUrl | string | Live URL to extract rendered meta (optional, compares source vs output) |
liveHtml | string | Raw HTML from local dev server (optional, for non-deployed pages) |
// Basic usage
improve_page_seo({
filePath: 'app/pages/tools/meta-checker.vue',
fileContent: '...',
topic: 'meta tag checker tool'
})
// With live comparison
improve_page_seo({
filePath: 'app/pages/tools/meta-checker.vue',
fileContent: '...',
liveUrl: 'https://mysite.com/tools/meta-checker'
})
// With local dev HTML
improve_page_seo({
filePath: 'app/pages/tools/meta-checker.vue',
fileContent: '...',
liveHtml: '<html>...</html>' // from $fetch('http://localhost:3000/tools/meta-checker')
})
The prompt runs these tools in sequence:
Returns:
liveUrl/liveHtml provided) - Comparison of what's in source vs what actually rendersuseSeoMeta() with researched keywordsuseSchemaOrg() for the page typedefineOgImage() configurationThe content prompts work together:
research_keywords → content_brief → article_generation
Generate a structured outline for an article. Use after keyword research.
| Parameter | Type | Description |
|---|---|---|
topic | string | Main topic for the article |
targetKeywords | string | Comma-separated target keywords (3-5) |
competitorUrls | string | Comma-separated URLs to differentiate from (optional) |
pageType | string | technical, marketing, or tutorial (default: technical) |
content_brief({
topic: 'Adding Schema.org to Nuxt Pages',
targetKeywords: 'nuxt schema.org, useSchemaOrg nuxt, nuxt structured data',
pageType: 'technical'
})
The prompt returns:
The brief follows the Writing Guide:
Generate a full article from an outline. Embeds the complete Writing Guide rules.
| Parameter | Type | Default | Description |
|---|---|---|---|
outline | string | required | Content brief from content_brief |
targetWordCount | string | "1500" | Target length (500-5000) |
includeCodeExamples | string | "true" | Include code examples (true/false) |
sitemapUrls | string | - | Comma-separated site URLs for internal linking |
article_generation({
outline: '# Content Brief\n\n## Primary Intent...',
targetWordCount: '2000',
includeCodeExamples: 'true',
sitemapUrls: '/docs/schema-org/getting-started, /docs/seo-utils/api/use-seo-meta'
})
The prompt includes:
Banned words: dive into, crucial, leverage, ensure, comprehensive...
Banned phrases: "it's important to note", "in today's X", "let's explore"...
Quick fixes:
| Slop | Fix |
|---|---|
| It's important to note... | just state it |
| This allows you to... | You can... |
| In order to... | To... |
Voice rules:
Structure rules:
The output includes markers for content that needs follow-up:
[STAT NEEDED: percentage of sites with broken meta tags]
[VERIFY: does this work in Nuxt 4?]
[EXAMPLE NEEDED: real-world product schema]
[LINK: internal link to related page]
Review the article and fill these gaps before publishing.
A full workflow for creating a new article:
// 1. Research keywords
const keywords = await research_keywords({ topic: 'nuxt meta tags' })
// 2. Create outline
const brief = await content_brief({
topic: 'Adding Meta Tags in Nuxt',
targetKeywords: keywords.keywords.map(k => k.keyword).slice(0, 5).join(', ')
})
// 3. Generate article
const article = await article_generation({
outline: brief,
targetWordCount: '1500'
})
Keyword Research
MCP tools for keyword research and SERP analysis. Find long-tail keywords, analyze competition, and discover content gaps—through your AI assistant.
Social Signals
MCP tool for analyzing social proof and community activity. Validate market demand across GitHub, Reddit, and Twitter before building.