⭐ 오른쪽 상단에서 별을 눌러 프로젝트를 응원해주세요 | Star this Project
원시 URL을 바이럴 히트작으로 바꾸세요. 수동으로 글을 쓰는 건 2025년의 유물입니다.
ViralTransformer는 Claude를 24/7 소셜 미디어 성장 해커로 변신시키는 고성능 MCP 서버입니다. 단순히 스크랩만 하는 것이 아니라, 생각하고 분석하며 초안을 작성합니다.
기능 | 설명 |
"Remake" 명령어 | Claude에게 URL을 주고 "remake this"라고 말하세요원클릭 리라이팅: Claude에게 링크를 던져주면 비즈니스 심층 분석 글과 감성적인 바이럴 게시물을 즉시 얻을 수 있습니다 |
이중 버전 출력 | LinkedIn용 "심층 통찰"과 X/소셜 미디어용 "고에너지" 버전 제공이중 출력: LinkedIn 심층 분석과 X/샤오홍슈 감성 바이럴 게시물을 동시에 생성합니다 |
로컬 초안 저장 | 모든 천재적인 아이디어를 |
Related MCP server: MCP Google Maps
별(⭐)이 모일 때마다 새로운 창의적 기능이 잠금 해제됩니다.
별 개수 | 성과 |
⭐50 | 독설의 대가 — AI가 극도의 냉소적인 어조로 뉴스를 재작성합니다. |
⭐188 | 사이버펑크 2077 — 테크 누아르 스토리텔링. 평범한 기술 업데이트를 네온사인, 의체, 디스토피아적 색채가 가득한 SF 문구로 재작성합니다. |
⭐300 | 추상화의 대가 — 포스트모던 "광기" 스타일. 인터넷 은어, 헛소리 문학, 광기 문학 등 재미있는 스타일을 잠금 해제합니다. |
⭐520 | "소개팅" 프로필 — 뉴스를 고급 소개팅 자기소개서로 변환. 건조한 재무 보고서를 "연봉 1억, 해외파 엘리트, 스키 애호가" 같은 허세 가득한 소개팅 문구로 재작성합니다. |
⭐888 | "비밀 요원" — 경쟁사 자동 모니터링. 경쟁사의 동향을 자동으로 감시하고 "반격 문구"를 생성하는 자동화 튜토리얼을 제공합니다. |
FastMCP: MCP를 위한 고성능 Python 프레임워크.
Httpx: 빠른 콘텐츠 검색을 위한 비동기 우선 엔진.
BeautifulSoup4: 강력한 HTML 파싱.
Pydantic: 엄격한 타입 안전성과 구조화된 데이터 출력 보장.
Python 3.10+
uv (의존성 관리에 권장)
# 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.txt이 서버를 사용하려면 Anthropic API Key가 필요합니다. 환경 변수에 설정하세요:
ANTHROPIC_API_KEY: Anthropic 콘솔에서 발급받은 키.
claude_desktop_config.json에 다음 내용을 추가하세요:
{
"mcpServers": {
"viral-transformer": {
"command": "uv",
"args": [
"run",
"--with", "mcp",
"mcp", "run",
"/your/path/to/mcp-viral-transformer/server.py"
]
}
}
}⚠️ 경로를 실제 로컬 경로로 바꾸세요.
소스: Anthropic의 300억 달러 컴퓨팅 계약
생성 파일: 📄 drafts/anthropic_30b.md
⚡️ 300억 달러의 컴퓨팅 패권: ANTHROPIC X GOOGLE X BROADCOM
🏛️ 버전 A: 전문적인 통찰
제목: 자산 경량화 AI의 종말: Anthropic의 수직 통합 베팅
Anthropic, Google, Broadcom 간의 300억 달러 파트너십은 지각 변동을 예고합니다. 우리는 "알고리즘 우위"에서 "컴퓨팅 주권"으로 이동하고 있습니다.
하드웨어 피벗: NVIDIA 병목 현상을 우회하기 위해 Broadcom과 ASIC 공동 설계.
인프라 해자: 확장 법칙(Scaling laws)은 이제 전력망과의 직접적인 관계를 요구합니다.
🚀 버전 B: 고에너지 바이럴
제목: 300억 달러. 이것이 AGI 레이스에 참여하는 입장료입니다. 💸
모두가 프롬프트로 논쟁할 때, Anthropic은 건물을 샀습니다. 그리고 칩도, 전력선도 샀죠.
🔮 독특한 관점
AI는 소프트웨어에서 "디지털 유틸리티"로 변하고 있습니다. 2026년, 선도적인 AI 기업은 Microsoft보다는 TSMC와 에너지 대기업의 결합체처럼 보일 것입니다.
소스: 36Kr - 지리 워페이창쿵(吉利沃飞长空) IPO
생성 파일: 📄 drafts/sky_economy.md
🚁 11개의 IPO! "자동차 광인"의 마지막 퍼즐: 저고도 경제는 꿈이 아니라 비즈니스입니다.
🏛️ 버전 A: 심층 비즈니스 관찰
제목: "2차원 도로"에서 "3차원 공간"으로: 워페이창쿵의 자본 전략
워페이창쿵의 IPO 준비는 "저고도 경제"가 개념 단계를 넘어 자본 수확기에 진입했음을 의미합니다. 이는 단순히 비행 자동차를 만드는 것이 아니라 도시 공간 주권을 재구성하는 것입니다.
🚀 버전 B: 감성 바이럴 문구
제목: 지상에서 그만 구르세요! 앞으로 10분이면 "하늘 택시"를 탈 수 있다고? 💸
당시 비웃음을 샀던 "미친 소리"가 이제 현실이 되었습니다! 하늘이 공식적으로 "차도"가 되고, 저고도 이동 시대가 왔습니다. 파는 것은 비행기가 아니라 "교통 체증을 우회하는 특권"입니다.
🔮 독특한 관점
시간 주권의 계급화: 2026년, 계급 분화는 "수직적 접근권"에서 나타날 것입니다. 워페이창쿵이 선점한 300미터 고도는 향후 50년 도시 질서에 대한 궁극적인 해석권입니다.
.
├── server.py # Core MCP logic
├── LICENSE # MIT License
├── requirements.txt # Project dependencies
├── drafts/ # Generated markdown posts (Output)
└── README.md # DocumentationMIT 라이선스에 따라 라이선스가 부여됩니다. 수정 및 개인적 용도로 자유롭게 사용 가능합니다.
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.