yt-analysis-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: ask_about_video answers questions about content, extract_frames extracts frames at user-provided timestamps, extract_screenshots uses AI to find and extract key frames, get_video_timestamps previews AI-identified timestamps, and summarize_video provides text summaries. The descriptions explicitly differentiate their use cases, such as distinguishing between extract_frames (manual timestamps) and extract_screenshots (AI-selected timestamps).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with underscores, using clear action verbs like ask_about, extract, get, and summarize paired with descriptive nouns like video, frames, screenshots, and timestamps. There are no deviations in style or convention, making the set predictable and easy to understand at a glance.
Tool Count5/5
With 5 tools, the count is well-scoped for the YouTube analysis domain, covering core workflows like content querying, frame extraction (both manual and AI-driven), timestamp previewing, and summarization. Each tool earns its place without redundancy, and the number is manageable for agents to navigate effectively.
Completeness4/5
The tool set provides strong coverage for video analysis, including content understanding, visual extraction, and summarization, with no dead ends. A minor gap exists in lacking direct video metadata retrieval (e.g., title, duration, uploader), but agents can work around this by using existing tools like ask_about_video or summarize_video for such information.
Average 3.8/5 across 5 of 5 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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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 states the tool returns an answer based on the video, but does not describe how it processes the video (e.g., via AI analysis, transcript parsing), potential limitations (e.g., accuracy, language support), or operational traits like rate limits or authentication needs. 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and outcome. Every word earns its place with no redundancy or fluff, making it efficient and easy to parse for an AI agent.
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 of video content analysis and the lack of annotations and output schema, the description is incomplete. It does not explain the nature of the returned answer (e.g., text summary, timestamped response) or address potential issues like video length limits or unsupported content. For a tool with no structured behavioral data, more context is needed to ensure proper usage.
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 input schema has 100% description coverage, with clear documentation for both parameters ('youtube_url' and 'question'). The description adds no additional semantic details beyond what the schema provides, such as examples of valid questions or URL formats. Given the high schema coverage, a baseline score of 3 is appropriate, as the description does not compensate but also does not 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: 'Ask a specific question about a YouTube video's content. Returns an answer based on the video.' It specifies the verb ('Ask'), resource ('YouTube video's content'), and outcome ('Returns an answer'), which is clear and actionable. However, it does not explicitly differentiate from siblings like 'summarize_video' or 'get_video_timestamps', which might also involve video content analysis, so it misses full sibling distinction.
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 lacks explicit instructions on when to choose 'ask_about_video' over siblings such as 'summarize_video' for general overviews or 'get_video_timestamps' for temporal queries. There is no mention of prerequisites, exclusions, or comparative contexts, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 the tool 'Returns a text summary based on the specified detail level', which implies a read-only operation without side effects. However, it doesn't mention potential limitations such as video length constraints, processing time, authentication needs, or rate limits. The description is adequate but lacks depth for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise and front-loaded, consisting of just two sentences that directly state the tool's function and output. Every word earns its place, with no redundant or vague phrasing. It efficiently communicates the core purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally complete. It covers the basic action and output type but lacks details on error handling, summary format (e.g., bullet points vs. paragraphs), or example outputs. Without an output schema, more guidance on return values would be beneficial, but the description meets the minimum viable threshold.
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 input schema has 100% description coverage, with clear documentation for 'youtube_url' (URL formats) and 'detail_level' (enum values with explanations). The description adds minimal value beyond the schema, only reiterating that summarization is 'based on the specified detail level'. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
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: 'Summarize a YouTube video's content' with the verb 'summarize' and resource 'YouTube video'. It distinguishes from siblings like 'ask_about_video' (Q&A), 'extract_frames' (image extraction), 'extract_screenshots' (screenshot capture), and 'get_video_timestamps' (timestamp listing) by focusing on textual summarization. However, it doesn't explicitly contrast with these alternatives, 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 its siblings. It mentions a 'detail level' parameter but doesn't explain scenarios where 'brief', 'medium', or 'detailed' summaries are appropriate, nor does it reference alternatives like 'ask_about_video' for specific queries. This lack of contextual usage advice limits its effectiveness for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It mentions the action ('extract frames') and context for timestamps, but lacks details on behavioral traits such as rate limits, authentication needs, file output behavior (e.g., format, naming), or error handling. It adds some value with the timestamp context but misses key operational aspects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose and followed by usage guidance. Every sentence earns its place by providing essential information without redundancy, making it highly efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 for a tool with 4 parameters and mutation-like behavior (extracting frames implies file creation). It covers purpose and usage well but lacks details on output format, error cases, or side effects, leaving gaps in operational context.
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 parameters. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't explain timestamp precision or URL validation). Baseline 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Extract frames from a YouTube video') and resource ('YouTube video'), distinguishing it from sibling tools like 'extract_screenshots' (which might extract continuous screenshots) and 'get_video_timestamps' (which provides timestamps rather than frames). The verb 'extract' is precise and the scope is well-defined.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use this tool ('when you already know the exact timestamps you want') and provides examples of alternative sources ('e.g., from get_video_timestamps or video summary'), clearly differentiating it from tools that might infer or generate timestamps. This gives clear context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 adequately describes the core behavior (AI-driven timestamp selection, frame extraction, base64 return, optional disk saving) but lacks details about error handling, rate limits, authentication requirements, processing time, or what constitutes 'important moments.' The description doesn't contradict any annotations since none exist, but could provide more operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and well-structured in two sentences. The first sentence establishes the core functionality, the second explains the dual output mechanism. Every word earns its place with no redundancy or unnecessary elaboration, making it easy to parse while being informationally dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (5 parameters, AI processing, dual output) and absence of both annotations and output schema, the description is adequate but incomplete. It covers the what and how but lacks information about return format details (structure of base64 response), error conditions, performance characteristics, or dependencies. For a tool with no output schema, more detail about return values would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context about parameter usage beyond the 100% schema coverage. It explains that screenshots are extracted 'at important moments' (relating to the 'focus' parameter's purpose), mentions AI-driven selection (context for 'count' and 'focus'), and notes the dual output (base64 and optional disk saving) which helps understand 'output_dir' usage. While the schema fully documents parameters, the description provides valuable semantic framing.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('extract key screenshots', 'uses AI to identify visually significant timestamps', 'extracts frames') and resources ('from a YouTube video'). It distinguishes from sibling tools like 'extract_frames' by specifying AI-driven selection of important moments rather than manual frame extraction, and from 'get_video_timestamps' by including screenshot extraction functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool (extracting AI-selected key screenshots from YouTube videos) and implicitly distinguishes from alternatives like 'extract_frames' (manual extraction) and 'get_video_timestamps' (timestamp-only output). However, it doesn't explicitly state when NOT to use this tool or provide direct comparison statements like 'use X instead for Y scenario'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it uses AI for analysis, identifies important moments, returns timestamps (not frames), and operates in preview mode. It doesn't mention rate limits, authentication needs, or error handling, but covers the core functionality adequately.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that are front-loaded with the core purpose and usage context. Every word earns its place, with no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (AI analysis of video content), no annotations, and no output schema, the description does well by explaining the preview nature and output format (timestamps). It could benefit from mentioning the return structure or error cases, but it's largely complete for guiding agent usage.
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%, so the schema already fully documents all three parameters. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate coverage through structured data alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('identify important moments'), resource ('YouTube video'), and output ('return their timestamps'), while explicitly distinguishing it from sibling tools by stating it works 'WITHOUT extracting frames' and is for 'preview mode' before committing to extraction tools like extract_frames or extract_screenshots.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool ('preview mode... before committing to extraction') and when not to use it ('WITHOUT extracting frames'), with clear alternatives implied through sibling tools like extract_frames and extract_screenshots for actual extraction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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