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 tool 'can be a cache lookup' and 'can be more reliable than scraping,' which hints at performance and reliability traits. However, it doesn't cover critical aspects like rate limits, authentication needs, error handling, or data freshness, leaving gaps in transparency for a web data tool.

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 with three concise sentences that are front-loaded: the first states the purpose, the second specifies a requirement, and the third adds behavioral context. There's no wasted text, and each sentence adds value, though it could be slightly more structured for clarity.

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 complexity (web data retrieval with 2 parameters), no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on return values, error cases, limitations (e.g., comment count max), and how it differs from scraping siblings. For a data-fetching tool, this leaves significant gaps in understanding.

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%, so the description must compensate for undocumented parameters. It only mentions the 'url' parameter ('Requires a valid youtube video URL') and implies data retrieval but doesn't explain 'num_of_comments' or its default value. With 2 parameters and minimal semantic detail, it fails to adequately supplement the schema, resulting in a low score.

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 comments data.' It specifies the verb ('read'), resource ('youtube comments data'), and quality ('structured'). However, it doesn't explicitly distinguish from sibling tools like 'web_data_youtube_videos' or other comment-related tools, which prevents a perfect score.

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 mentions it 'can be more reliable than scraping,' which implies it's a better alternative to scraping tools. However, it doesn't explicitly state when to use this versus other sibling tools (e.g., 'scrape_as_html' for YouTube) or detail specific scenarios, leaving usage somewhat implied rather than explicit.

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