Bright Data Web MCP

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 adds useful context: the requirement for a valid URL, the cache lookup behavior (implying potential speed/reliability benefits), and the comparison to scraping. However, it doesn't cover key behavioral aspects like error handling, rate limits, authentication needs, or what 'structured data' specifically includes (e.g., metadata fields).

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 highly concise and front-loaded: the first sentence states the core purpose, followed by prerequisites and behavioral context. Every sentence adds value without redundancy, making it efficient and easy to parse.

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's moderate complexity (single parameter, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose, input requirement, and a key behavioral trait (cache lookup), but lacks details on output structure, error cases, or how it differs from sibling tools like 'scrape_as_html' for YouTube data.

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 1 parameter with 0% description coverage. The description adds semantic context by specifying that the 'url' parameter must be 'a valid YouTube video URL,' which clarifies the expected format beyond the schema's generic 'uri' format. This compensates partially for the low schema coverage, but doesn't provide examples or detailed 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: 'Quickly read structured YouTube videos data.' It specifies the verb ('read') and resource ('structured YouTube videos data'), making the action clear. However, it doesn't explicitly differentiate from sibling tools like 'web_data_youtube_comments' or 'web_data_youtube_profiles' beyond the 'videos' focus.

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 some usage context: 'Requires a valid YouTube video URL' and 'This can be a cache lookup, so it can be more reliable than scraping.' This implies when to use it (for structured video data with URL) and hints at an advantage over scraping tools. However, it doesn't explicitly state when to choose this over alternatives like 'scrape_as_html' or other web_data tools, nor does it mention exclusions.

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