yt-analysis-mcp

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.

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.

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.

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.

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.

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.