bright_data_web_search_tool

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description is the sole source of behavioral disclosure. It mentions searching and returning results but does not disclose any potential side effects, rate limits, authentication needs, or failure modes. The description only covers input and output, not behavioral traits.

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 concise and front-loaded: the first sentence states the purpose, the second gives usage guidance, and the input/output block is clearly structured. No unnecessary words.

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?

For a simple search tool with one parameter and no output schema, the description adequately covers purpose, usage, and input/output. However, it does not specify the number of results returned or behavior when no results are found, which would improve completeness.

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 has only one parameter, 'query', with no description. The description clarifies that the query is 'The user query to search for information,' which adds semantic meaning beyond the parameter name. It also describes the output format, aiding understanding.

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 uses the specific verb 'Search' and identifies the resource ('information on a given topic using Bright Data'). It distinguishes from the sibling faq_retrieval_tool by stating it is for topics 'not related to general machine learning.'

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 explicitly states when to use the tool: 'Use this tool when the user asks about a specific topic or question that is not related to general machine learning.' This provides a clear conditional and implies an alternative for ML-related queries.

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