youtube_video_related
Related videos for a given video id.
Bulk support: accepts ids for batched execution.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| ids | No | ||
| token | No |
Related videos for a given video id.
Bulk support: accepts ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| ids | No | ||
| token | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering the safety profile. The description adds the bulk execution capability (accepts 'ids' for batching) which is a behavioral trait not in annotations. However, it does not mention behavior like pagination, required authentication, or what happens with invalid IDs. Annotations carry most of the burden, and the added context is minimal but accurate.
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 at two sentences, front-loading the core purpose in the first sentence and adding bulk support in the second. Every word is informative. However, it could have included a brief mention of 'token' without sacrificing conciseness, but it is appropriately sized for the tool's simplicity.
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?
The tool has no output schemaalert, and the description does not explain what the response looks like (e.g., list of video objects with titles, thumbs?). With 0% parameter schema coverage and no output schema, the description should provide more context on return values or additional behavior. Its simplicity works for a basic read tool, but it lacks details like pagination, error cases, and auth requirements, making it incomplete for an agent to use confidently.
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 description coverage is 0%, meaning neither the description nor the schema provides explanations for 'id', 'ids', or 'token'. The description only mentions 'id' implicitly and 'ids' in the bulk note, but does not explain their relationship (e.g., precedence, mutual exclusivity) or the purpose of 'token'. With 3 parameters and no coverage, the description fails to compensate, leaving the agent without critical parameter semantics.
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 identifies the tool as retrieving related videos for a given video ID, using a specific verb ('Related videos') and resource ('video id'). It distinguishes from siblings by focusing on the 'related' concept, but it doesn't explicitly contrast with similar tools like youtube_home_feed or youtube_search, which could fetch recommendations.
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 implies usage when a user wants related videos for a specific video ID and mentions bulk support for batched execution. It does not explicitly state when not to use it (e.g., if the user wants search results or channel content), but the sibling tool list provides alternative context. The guidance is clear enough for typical scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.