google_serp_short_videos: GET /

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 mentions 'scrapes' (indicating a read-only external fetch), pagination, and the returned fields. However, it omits potential behavioral specifics such as rate limits, authentication requirements, or whether results are cached or live. While not misleading, it lacks depth on operational constraints.

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 well-structured and front-loaded with the core action and scope. The first sentence is direct, followed by a concise list of parameters and use cases. It is slightly verbose with the five-item use-case list, but overall it is efficient and avoids unnecessary fluff.

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 tool has 9 parameters and no output schema, the description is sufficiently detailed. It names the output fields (title, thumbnail, duration, etc.) and covers the main parameter groups. It does not mention pagination limits or size, but these are minor and likely not required for an agent to invoke it correctly. Overall, it provides enough context for effective use.

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% coverage with detailed descriptions for every parameter. The description adds only high-level categorization (location/uule, country gl/cr, language hl/lr, device type, page), which is useful for grouping but does not explain nuances like the difference between gl and cr or hl and lr beyond what the schema already provides. Since the schema carries the load, the description adds marginal semantic value.

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 explicitly states what the tool does: it scrapes the Google Short Videos carousel for a query, listing the specific content sources (TikTok, YouTube Shorts, Instagram Reels) and the types of results returned. The name and title reinforce this, but the description adds detail that distinguishes it from sibling tools focused on products, shopping, news, or standard search results.

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 lists concrete use cases (short-form content discovery, viral-trend monitoring, influencer research, cross-platform aggregation, and sourcing clips for LLM responses), making the intended usage clear. It does not explicitly mention alternatives, but the sibling tools are semantically distinct (e.g., news, products), so the usage context is unambiguous. Lacks explicit 'when not to use' guidance, but the purpose is narrowly defined.

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