get-segmented-transcript

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 effectively describes key behaviors: it splits transcripts into equal time segments, extracts text with timestamps, returns markdown-formatted output, and specifies defaults (segmentCount default: 4) and limits (max: 10). However, it does not cover potential errors (e.g., invalid videoId) or performance aspects like rate limits, leaving some gaps.

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 appropriately sized and front-loaded, with the core purpose in the first sentence, usage context in the second, and parameter details in the third. Every sentence adds value without redundancy, 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 (a processing tool with 2 parameters), no annotations, and no output schema, the description does well by covering purpose, usage, parameters, and output format. However, it lacks details on error handling or edge cases (e.g., what happens with very short videos), which would make it more complete for safe invocation.

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 schema description coverage is 0%, so the description must fully compensate. It does so by clearly explaining both parameters: 'videoId (required) - The YouTube video ID' and 'segmentCount (optional) - Number of segments to divide the video into (default: 4, max: 10)'. This adds essential meaning beyond the bare schema, including requirements, defaults, and constraints.

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 with specific verbs ('divide', 'split', 'extract') and resource ('video transcript'), distinguishing it from siblings like 'get-video-transcript' (which likely returns the full transcript) and 'get-key-moments' (which likely identifies highlights rather than equal segments). It explicitly mentions what the tool does beyond just retrieving a transcript.

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 clear context for when to use this tool ('for easier analysis and navigation', 'ideal for analyzing the structure of longer videos or when you need to focus on specific parts'), but it does not explicitly mention when not to use it or name alternatives (e.g., 'get-video-transcript' for the full transcript). This gives good guidance but lacks explicit exclusions or sibling comparisons.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.