Fallback Web Search

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

With no annotations, the description carries the behavioral disclosure burden. It adds useful facts beyond the schema: 'zero-cost' and the anti-poisoning filter applied to snippets. It does not, however, explain limitations, failure behavior, or what the filtered result set looks like.

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?

Two tight sentences deliver the core purpose, scope, cost trait, and filtering behavior with no filler. The most decision-relevant information is front-loaded.

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 two-parameter search tool with an output schema, the description is largely sufficient for an agent to invoke it correctly. The main gap is the underspecified fallback trigger, but the operational context is otherwise clear.

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%, and the description does not explain either parameter. While 'query' and 'max_results' are fairly self-explanatory from names and schema defaults, the description adds no meaning beyond what the input schema already provides.

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 performs a DuckDuckGo search and specifies topical scope ('recent news, product launches, and general topics'). It is distinguishable from academic/repository-focused siblings by the explicit 'general topics' scope, though it does not name a specific sibling for contrast.

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 word 'fallback' implies this tool is used when primary search options are unavailable or unsuitable, and the topic list gives useful context. However, it never explicitly states when to use this tool versus alternatives or when not to use it, leaving the fallback trigger to inference.

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