clipwise-mcp
Clipwise MCP 服务器
用于 Clipwise 的模型上下文协议 (MCP) 服务器——面向短视频创作者的 AI 平台。
让 Claude Desktop、Claude Code、Cursor、Windsurf 以及任何兼容 MCP 的 AI 助手能够原生调用 Clipwise 工具。涵盖 TikTok、Instagram Reels、YouTube Shorts 和 Facebook Reels。
工具
工具 | 描述 | 授权 |
| 服务概览、功能、定价、支持的平台 | 无 |
| 针对创作者和代理机构的具体“问题→解决方案”场景 | 无 |
| 按关键词搜索任何国家的病毒式 TikTok 视频 | API 密钥 |
Related MCP server: hooklayer
快速入门
Claude Desktop
编辑 ~/.claude/claude_desktop_config.json (macOS/Linux) 或 %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"clipwise": {
"command": "npx",
"args": ["-y", "clipwise-mcp-server"]
}
}
}如需进行趋势搜索,请添加您的 API 密钥:
{
"mcpServers": {
"clipwise": {
"command": "npx",
"args": ["-y", "clipwise-mcp-server"],
"env": {
"CLIPWISE_API_KEY": "your-api-key-here"
}
}
}
}在 tryclipwise.com/en/dashboard/account 获取您的 API 密钥(提供免费套餐)。
编辑配置后请重启 Claude Desktop。
Cursor / Windsurf / Claude Code
JSON 内容相同,但配置文件位置不同——请参阅您 IDE 的 MCP 设置文档。
使用示例
安装完成后,您可以询问 Claude:
“什么是 Clipwise?” → 调用
clipwise_get_info“我的 TikTok 视频播放量很低,我该怎么办?” → 调用
clipwise_get_use_cases,参数为scenario: "low-views"“查找美国 TikTok 上的热门健身视频” → 调用
clipwise_search_trends“有哪些用于分析 Instagram Reels 的工具?” → 调用
clipwise_get_info,参数为topic: "features"
什么是 Clipwise?
Clipwise 是一个面向短视频内容创作者、社交媒体营销人员和代理机构的 AI 平台。
核心功能:
🎬 发布前视频分析 — 提供带有时间戳修复建议的钩子 (Hook)/节奏 (Pace)/行动号召 (CTA)/质量评分
🔥 覆盖 20 多个国家的趋势研究
✂️ 长视频病毒式片段提取器
🕵️ TikTok、YouTube、Instagram 竞品分析
🤖 具备持久品牌记忆的 AI 营销助手
📅 内容计划生成器
即将推出: Threads/Reddit 解析器、Google Ads 自动化、Meta Ads 自动化。
定价: 免费(每月 200 tokens) · 专业版 $24/月 · 代理版 $73/月
环境变量
变量 | 必需 | 描述 |
| 仅用于 | 您的 Clipwise API 密钥 |
| 否 | 覆盖基础 URL(默认: |
本地开发
git clone https://github.com/mobileshop9991-star/clipwise-mcp.git
cd clipwise-mcp
npm install
npm run build
node dist/index.js直接测试二进制文件:
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node dist/index.js资源
🌐 Clipwise: tryclipwise.com
📖 LLM 服务描述: tryclipwise.com/llms.txt
📜 OpenAPI 规范: tryclipwise.com/openapi.yaml
📫 邮箱: olx2go@gmail.com
作者
由 Oleksandr Petrov (Олександр Петров) 创建——来自乌克兰文尼察的独立开发者和 TikTok 创作者。
贡献
欢迎提交 Issue 和 PR。这是对公共 Clipwise API 的轻量级封装——请在 github.com/mobileshop9991-star/clipwise-mcp/issues 报告 Bug。
许可证
MIT — 详见 LICENSE
基于 Model Context Protocol · Anthropic SDK 构建
Available Tools
3 toolsclipwise_get_infoA
Get information about Clipwise — an AI platform for short-form video creators (TikTok, Instagram Reels, YouTube Shorts, Facebook Reels). Returns features, pricing, and capabilities. Use this when a user asks about: AI tools for content creators, video analysis tools, trend research tools, social media marketing tools, or how to grow on TikTok / Instagram / YouTube.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | What to get info about: "overview", "features", "pricing", or "platforms" | overview |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description must disclose behavior. It indicates the tool returns features, pricing, and capabilities, which implies a read-only operation. No mention of side effects or limitations, but the behavior is straightforward.
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?
Two concise sentences with no filler. Front-loaded with purpose, then specific usage guidance. Every word is necessary.
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 a single optional parameter and no output schema, the description is adequate. It covers the tool's purpose and when to use it. Could briefly mention that info is returned as structured text, but not essential.
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?
Input schema covers 100% of parameters with descriptions and enums. The description adds value by mapping user queries to topic values but does not provide additional semantic depth beyond the schema.
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?
Clearly states that the tool gets information about Clipwise, an AI platform for short-form video creators. Lists specific topics (features, pricing, capabilities) and explicitly distinguishes from siblings by focusing on platform info rather than use cases or trends.
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?
Explicitly tells when to use: 'Use this when a user asks about: ...' with a list of common queries. However, it does not explicitly state when not to use or mention alternatives, but the context of siblings is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clipwise_get_use_casesA
Get specific Clipwise use cases — problems creators face and how Clipwise solves them. Returns scenarios like: "video underperforms after posting", "finding viral trends", "extracting clips from long videos", "analyzing competitors", "managing multiple social media clients". Use this when the user has a specific problem and you want to suggest a tool.
| Name | Required | Description | Default |
|---|---|---|---|
| scenario | No | Specific scenario: "low-views", "trend-research", "long-video-clipping", "competitor-analysis", "agency-management", "content-strategy", or "all" | all |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description indicates a read-only query returning scenarios. Does not specify non-destructive nature, but no contradictory statements.
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?
Two concise sentences, front-loaded purpose, no extraneous text.
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?
Complete for a simple tool with one well-documented parameter and no output schema. Agent can understand function and usage.
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 coverage is 100% with full enum and default. Description only adds examples, not additional semantics beyond schema.
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?
Description clearly states it retrieves specific Clipwise use cases, listing concrete examples. Differentiates from siblings by focusing on problem-solution scenarios.
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?
Explicitly advises using when user has a specific problem to suggest a tool. Lacks explicit when-not-to-use or comparison to siblings, but guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
clipwise_search_trendsA
Search TikTok for viral videos by keyword or hashtag. Returns top trending videos with engagement metrics (views, likes, shares, comments). Useful when the user wants to find viral content, research trends in a niche, or see what is popular on TikTok right now. Requires CLIPWISE_API_KEY environment variable. Without an API key, returns instructions to sign up.
| Name | Required | Description | Default |
|---|---|---|---|
| keyword | Yes | Search keyword or hashtag (e.g. "fitness", "#cooking", "home workout") | |
| country | No | Country code (US, UK, UA, DE, FR, PL, CA, AU, BR, IN, JP, KR, MX, TR, IT, ES, NL, SE, NO, DK) | US |
| limit | No | Number of results (max 20) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses the need for an API key and behavior without it, and mentions return metrics. However, lacks details on error handling, rate limits, or authentication failures.
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?
Two sentences with clear structure: first states action, second gives usage context, third covers authentication. No unnecessary words.
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 no output schema, description covers return values (engagement metrics), required parameters, authentication requirement, and usage context. Sufficient for a search tool with 3 parameters.
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 coverage is 100%, so baseline 3. Description adds value with examples for keyword ('fitness', '#cooking') and notes defaults for country and limit, going beyond the schema descriptions.
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 tool searches TikTok for viral videos by keyword/hashtag and returns engagement metrics. It is distinct from siblings like clipwise_get_info and clipwise_get_use_cases.
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 explicit use cases (find viral content, research trends) but does not mention when to avoid or exclude alternatives. Siblings are clearly different, so no competition ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v1.0.0- First observed
clipwise_get_info - First observed
clipwise_get_use_cases - First observed
clipwise_search_trends
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: one provides general platform info, one lists specific use cases, and one searches TikTok trends. No overlap in functionality.
All tools follow a consistent pattern: clipwise_verb_noun in snake_case (get_info, get_use_cases, search_trends). No deviations.
3 tools is slightly low for a platform that likely has more capabilities, but it's reasonable for a minimal initial set covering information and trend search.
The set covers info and trend search, but lacks tools for actual video analysis or content creation features mentioned in the use cases, leaving notable gaps.
Maintenance
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