⭐ Нажмите, чтобы поставить звезду проекту | Star this Project
Превращайте обычные ссылки в виральные хиты. Ведь писать посты вручную — это так в духе 2025 года.
ViralTransformer — это высокопроизводительный MCP-сервер, который превращает Claude в круглосуточного хакера роста в социальных сетях. Он не просто парсит данные; он думает, анализирует и составляет черновики.
Функция | Описание |
Команда "Remake" | Дайте Claude ссылку и скажите "remake this"Переработка контента в один клик: получите глубокую бизнес-статью или эмоциональный виральный пост |
Двухверсионный вывод | Получайте "Глубокий анализ" для LinkedIn и "Энергичный пост" для X/соцсетейДвойной результат: одновременная генерация глубоких наблюдений и эмоционально заряженного контента |
Локальные черновики | Автоматическое сохранение каждой гениальной идеи в |
Related MCP server: MCP Google Maps
Каждая ⭐ открывает новые творческие возможности.
Звезды | Достижение |
⭐50 | Мастер сарказма — AI переписывает новости с едкой иронией |
⭐188 | Киберпанк 2077 — Техно-нуарное повествование |
⭐300 | Мастер абстракции — Постмодернистский стиль "безумия" |
⭐520 | Профиль для "Свиданий вслепую" — Новости как анкета элитного жениха/невесты |
⭐888 | "Секретный агент" — Автоматический мониторинг конкурентов |
FastMCP: Высокопроизводительный Python-фреймворк для MCP.
Httpx: Асинхронный движок для быстрого получения контента.
BeautifulSoup4: Надежный парсинг HTML.
Pydantic: Обеспечение строгой типизации и структурированного вывода данных.
Python 3.10+
uv (рекомендуется для управления зависимостями)
# Clone the repository
git clone https://github.com/BelleKou/mcp-viral-transformer.git
cd mcp-viral-transformer
# Install dependencies (Modern way)
uv pip install -e .
# Or the traditional way
pip install -r requirements.txtДля использования этого сервера вам понадобится API-ключ Anthropic. Установите его в переменные окружения:
ANTHROPIC_API_KEY: Ваш ключ из консоли Anthropic.
Добавьте следующее в ваш claude_desktop_config.json:
{
"mcpServers": {
"viral-transformer": {
"command": "uv",
"args": [
"run",
"--with", "mcp",
"mcp", "run",
"/your/path/to/mcp-viral-transformer/server.py"
]
}
}
}⚠️ Замените путь на ваш фактический локальный путь.
Источник: Сделка Anthropic на $30 млрд
Созданный файл: 📄 drafts/anthropic_30b.md
⚡️ ГЕГЕМОНИЯ ВЫЧИСЛИТЕЛЬНЫХ МОЩНОСТЕЙ ЗА $30 МЛРД: ANTHROPIC X GOOGLE X BROADCOM
🏛️ Версия A: Профессиональный анализ
Заголовок: Смерть AI-моделей без собственной инфраструктуры: ставка Anthropic на вертикальную интеграцию
Партнерство на $30 млрд между Anthropic, Google и Broadcom знаменует тектонический сдвиг. Мы уходим от "превосходства алгоритмов" к "суверенитету вычислительных мощностей".
Аппаратный разворот: Совместная разработка ASIC с Broadcom для обхода узкого места NVIDIA.
Инфраструктурный ров: Законы масштабирования теперь требуют прямой связи с энергосетями.
🚀 Версия B: Виральный пост
Заголовок: $30 МИЛЛИАРДОВ. Вот цена входа в гонку AGI. 💸
Пока все спорят о промптах, Anthropic просто купила здание. И чипы. И линии электропередач.
🔮 УНИКАЛЬНЫЙ УГОЛ ОБЗОРА
AI превращается из софта в "цифровую коммунальную услугу". В 2026 году ведущая AI-компания будет больше похожа на гибрид TSMC и энергетического конгломерата, чем на Microsoft.
Источник: 36Kr - IPO Geely Aerofugia
Созданный файл: 📄 drafts/sky_economy.md
🚁 11 IPO! Последний пазл "автомобильного безумца": экономика низких высот — это не мечта, это бизнес.
🏛️ Версия A: Глубокий бизнес-анализ
Заголовок: От "двумерных дорог" к "трехмерному пространству": капитализация Aerofugia
Подготовка Aerofugia к IPO знаменует переход "экономики низких высот" от концепции к стадии сбора капитала. Это не просто создание летающих автомобилей, это реструктуризация суверенитета городского пространства.
🚀 Версия B: Эмоциональный виральный пост
Заголовок: Хватит стоять в пробках! Полет на "летающем такси" за 10 минут? 💸
То, над чем раньше смеялись, стало реальностью! Небо официально становится "дорогой". Мы продаем не самолеты, а "привилегию обхода пробок".
🔮 УНИКАЛЬНЫЙ УГОЛ ОБЗОРА
Классовое разделение через временной суверенитет: в 2026 году социальное расслоение будет проявляться в "праве на вертикальный доступ". Высота 300 метров, которую захватывает Aerofugia, — это право на определение городского порядка на ближайшие 50 лет.
.
├── server.py # Core MCP logic
├── LICENSE # MIT License
├── requirements.txt # Project dependencies
├── drafts/ # Generated markdown posts (Output)
└── README.md # DocumentationЛицензировано по MIT License. Открыто для модификации и личного использования.
Saves content with a safe filename to the /drafts folder.
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | ||
| content | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'saves content' (implying a write operation) and mentions a 'safe filename', but doesn't clarify permissions, error handling, or what 'safe' entails. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's action and destination. It's front-loaded with the core purpose and has no wasted words, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which reduces the need to describe return values) but no annotations and 0% schema coverage, the description is minimally adequate. It covers the basic action and location but lacks details on behavior and parameters, making it incomplete for a mutation tool with undocumented inputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It mentions 'safe filename' and '/drafts folder', which adds some context for the 'filename' parameter, but doesn't explain 'content' or provide details on filename safety rules. This partial compensation is insufficient for the 2 undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Saves content') and target ('to the /drafts folder'), with the verb 'saves' being specific. However, it doesn't differentiate from the sibling tool 'scrape_article' (which appears unrelated), so it doesn't fully earn a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions saving to '/drafts folder' but doesn't specify use cases, prerequisites, or exclusions, leaving the agent with minimal context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scrapes clean content from a URL, focusing on the main article body.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool scrapes clean content and focuses on the main article body, which hints at behavior like content cleaning and body extraction. However, it lacks details on error handling, rate limits, authentication needs, or what 'clean' entails, leaving significant gaps for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core action and focus. Every word earns its place, with no redundancy or unnecessary elaboration, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (which covers return values), no annotations, and a simple input schema, the description is minimally adequate. It specifies the tool's focus on article body content, but for a scraping tool with no behavioral annotations, it could benefit from more context on limitations or expected output format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not mention the 'url' parameter explicitly, but with only 1 parameter and 0% schema description coverage, it compensates by implying the parameter's purpose through context ('from a URL'). This adds meaning beyond the bare schema, though it doesn't detail format or constraints, keeping it from a perfect score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('scrapes') and resource ('clean content from a URL'), specifying it focuses on the main article body. This distinguishes it from generic scraping tools, though it doesn't explicitly differentiate from the sibling 'save_draft' tool, which appears unrelated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as other scraping methods or tools. It mentions focusing on the main article body, which implies a context for article content extraction, but lacks explicit when/when-not instructions or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0save_draftscrape_articleScored across 2 tools
The two tools have completely distinct purposes: save_draft handles local file saving with safe naming, while scrape_article extracts clean content from URLs. There is no overlap in functionality or ambiguity between them.
Both tools follow a consistent verb_noun pattern (save_draft, scrape_article) with clear, descriptive names that align well with their functions. The naming style is uniform and predictable.
With only two tools, the server feels thin and under-scoped for a 'ViralTransformer' purpose, which implies content transformation or viral content handling. This minimal set limits functionality and suggests incomplete coverage of the domain.
The tool set is severely incomplete for a viral content transformation server. It lacks core operations like content generation, editing, publishing, analytics, or social media integration, leaving significant gaps that will hinder agent workflows.
PostHero MCP server — schedule and publish social media posts from any MCP-compatible AI tool
MCP server for QPost — lets AI agents publish video and image posts to YouTube, TikTok, Instagram.
WordPress MCP server: generate SEO posts, AI images, autoblog & WooCommerce on your self-hosted site
Social media MCP server: your Instagram, TikTok, YouTube, LinkedIn and Threads history for your AI.