Viral Shorts

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.2.2
Disambiguation2/5
Multiple tools have unclear boundaries and overlapping purposes. 'get_trending_topics' and 'get_youtube_shorts_trends' both focus on discovering trends/hot content with similar descriptions, while 'analyze_video_potential' and 'summarize_video_story' both analyze individual videos but with different aspects (performance metrics vs. content/story). This overlap could cause agent misselection confusion.
Naming Consistency4/5
The naming is mostly consistent with a clear verb_noun pattern (e.g., analyze_video_potential, discover_niche_trends). All tools use snake_case and descriptive names, with only minor deviations like 'get_youtube_shorts_trends' being slightly redundant compared to 'get_trending_topics'.
Tool Count5/5
With 5 tools, the count is well-scoped and appropriate for the server's purpose of YouTube Shorts analysis. Each tool appears to serve a distinct function within the domain, and the number is manageable without being too thin or overwhelming.
Completeness3/5
The tool surface covers analysis, trend discovery, and summarization for YouTube Shorts, but there are notable gaps. It lacks CRUD/lifecycle operations (e.g., no tools for creating, updating, or managing content), and the focus is primarily on reading/analyzing existing data, which may limit agent workflows for content creation or optimization.
Average 2.9/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions providing '详细的播放量、互动率等核心指标' (detailed core metrics like view count and engagement rate), which hints at read-only analysis, but doesn't cover aspects like data freshness, rate limits, authentication needs, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, consisting of two clear sentences that state the purpose and key outputs. There's no wasted text, and it efficiently communicates the core function. However, it could be slightly more structured by explicitly separating purpose from output details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing video performance), lack of annotations, and no output schema, the description is incomplete. It mentions metrics but doesn't detail the return structure, data sources, or limitations. For a tool that likely involves data retrieval and analysis, more context on behavior and outputs is needed to be fully helpful to an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'video_url' well-documented in the schema as 'YouTube Shorts 视频链接' (YouTube Shorts video link). The description adds no additional meaning beyond this, such as URL format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '深度分析单个 YouTube Shorts 视频的表现' (deeply analyze the performance of a single YouTube Shorts video). It specifies the verb '分析' (analyze) and resource 'YouTube Shorts 视频' (YouTube Shorts video), though it doesn't explicitly differentiate from sibling tools like 'summarize_video_story' or 'get_youtube_shorts_trends' which might have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 analyzing '单个' (single) videos, but doesn't specify contexts like performance evaluation, content strategy, or comparison with other tools. With siblings like 'get_youtube_shorts_trends' (likely for trends) and 'summarize_video_story' (likely for content), there's no explicit when/when-not or alternative recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the tool's function (discovering niche trends by analyzing video titles) and mentions '高 VPH' (high VPH, likely meaning high views per hour), but lacks details on permissions, rate limits, data sources, or output format. For a tool with no annotations and no output schema, this leaves significant gaps in understanding how the tool behaves operationally.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, stating the core purpose in the first sentence. It uses two sentences total: one for the main function and one for an example, with no redundant information. However, it could be slightly more structured by explicitly separating purpose from method, but overall it's efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (analyzing video data to identify trends), lack of annotations, and no output schema, the description is incomplete. It explains what the tool does but fails to cover behavioral aspects like data sources, processing time, error handling, or output format. For a tool with 4 parameters and no structured output information, more context is needed to ensure the agent can use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, meaning all parameters are well-documented in the input schema. The description adds minimal value beyond the schema, as it only implicitly references 'main_topic' through examples and doesn't elaborate on parameter interactions or usage nuances. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '智能发现某个主题下的细分爆款领域' (intelligently discover niche trending areas under a specific topic). It specifies the method ('通过分析大量视频标题,自动识别高 VPH 的关键词组合' - by analyzing many video titles, automatically identify high VPH keyword combinations) and provides concrete examples ('例如在 'AI' 主题下发现 'AI Kpop'、'AI 印度故事' 等细分趋势' - e.g., discovering 'AI Kpop', 'AI Indian stories' under the 'AI' topic). However, it doesn't explicitly differentiate from sibling tools like 'get_trending_topics' or 'get_youtube_shorts_trends', which might also involve trend analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives. It mentions analyzing video titles to find niche trends but doesn't compare it to sibling tools such as 'analyze_video_potential' or 'get_trending_topics', leaving the agent to infer usage based on general context. There are no exclusions or prerequisites stated, which limits practical application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions discovering and analyzing trends, it doesn't describe what the tool actually returns (e.g., list of topics, metrics, examples), how it sources data, whether it requires authentication, rate limits, or freshness of data. The description is too vague about the tool's actual behavior beyond the high-level purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that directly state the tool's purpose. There's no unnecessary information or repetition. However, it could be slightly more front-loaded by immediately clarifying the scope (YouTube Shorts) rather than having it in the middle of the first sentence.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (crucial for a discovery/analysis tool), how results are structured, data sources, or limitations. The purpose is clear, but the description lacks essential context about the tool's behavior and outputs that would help an agent use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents both parameters (category with enum values and hours_ago with range constraints). The description doesn't add any parameter-specific information beyond what's in the schema. It mentions '多个分类' (multiple categories) which aligns with the category parameter, but provides no additional context about parameter usage or semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '发现当前 YouTube Shorts 上的热门话题和趋势' (discover current trending topics and trends on YouTube Shorts) and '分析多个分类的热门内容,帮助选题' (analyze trending content across multiple categories to help with topic selection). It specifies the resource (YouTube Shorts trending topics) and the action (discover/analyze). However, it doesn't explicitly differentiate from sibling tools like 'get_youtube_shorts_trends' or 'discover_niche_trends', which appear related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 analyzing multiple categories to help with topic selection, but doesn't specify when this tool is appropriate compared to siblings like 'get_youtube_shorts_trends' (which appears similar) or 'discover_niche_trends' (which might focus on different aspects of trends). There's no mention of prerequisites, limitations, or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analyzing metrics (views, engagement, publish time) to identify high-quality short videos, but doesn't describe what the tool actually returns (e.g., list of videos with metadata), whether it requires authentication, rate limits, or how it sources data. For a tool with no annotation coverage, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose in the first sentence. The second sentence adds useful context about the analysis methodology. Both sentences earn their place, though it could be slightly more structured (e.g., explicitly stating it returns a list of videos).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't specify what the tool returns (e.g., video IDs, titles, metrics), making it hard for an agent to understand the result format. For a tool with 4 parameters and complex functionality (trend analysis), more contextual information about outputs and behavior is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema. It doesn't explain how parameters interact (e.g., how keyword filtering combines with time range) or provide usage examples. Baseline 3 is appropriate when schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '发现 YouTube Shorts 上的潜在热门视频' (discover potential trending videos on YouTube Shorts). It specifies the resource (YouTube Shorts videos) and the action (discover/identify trending content). However, it doesn't explicitly differentiate from sibling tools like 'get_trending_topics' or 'discover_niche_trends', which likely have overlapping domains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 its siblings. While it mentions analyzing metrics like views, engagement, and publish time, it doesn't specify scenarios where this tool is preferred over alternatives like 'analyze_video_potential' or 'get_trending_topics'. There's no explicit when/when-not usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool analyzes video content based on title and description, implying a read-only operation that doesn't modify data. However, it lacks details on rate limits, authentication needs, output format, error handling, or whether it accesses external APIs. For a tool with no annotation coverage, this is a significant gap in transparency about how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences that directly state the tool's function and analysis scope. It's front-loaded with the core purpose and avoids unnecessary details. However, it could be slightly more structured by explicitly separating the 'what' from the 'how' (e.g., clarifying it's for narrative analysis only), but it's efficient with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (analyzing video content narratively), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., structured summary, key points list), potential limitations (e.g., language support, video length constraints), or error cases. For a tool with no structured behavioral or output data, the description should provide more context to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100% (the 'video_url' parameter is fully described in the schema as 'YouTube Shorts 视频链接'), so the baseline is 3. The description adds no additional parameter semantics beyond what's in the schema—it doesn't specify URL format requirements, validation rules, or examples. It merely reiterates the tool's purpose without enhancing parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '提炼 YouTube Shorts 视频的故事梗概和核心内容' (summarize the story outline and core content of YouTube Shorts videos) and specifies it analyzes '主题、情节和创作手法' (theme, plot, and creative techniques). It distinguishes from siblings like 'analyze_video_potential' or 'get_trending_topics' by focusing on narrative analysis rather than trend discovery or potential assessment. However, it doesn't explicitly contrast with all siblings, preventing a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 analyzing 'YouTube Shorts' videos specifically, but doesn't clarify if it's for short-form content only, how it differs from 'analyze_video_potential' (which might assess virality or engagement), or when to choose it over trend-related siblings. There's an implied context (video analysis) but no explicit usage rules or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![viral-shorts MCP server](https://glama.ai/mcp/servers/Xeron2000/viral-shorts/badges/card.svg)](https://glama.ai/mcp/servers/Xeron2000/viral-shorts)

Score Badge

Copy to your README.md:

[![viral-shorts MCP server](https://glama.ai/mcp/servers/Xeron2000/viral-shorts/badges/score.svg)](https://glama.ai/mcp/servers/Xeron2000/viral-shorts)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/Xeron2000/viral-shorts'

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