web_search

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

With no annotations, the description carries the full disclosure burden, and it does well: it names the return fields, reveals the parallel multi-engine backend (DuckDuckGo, Mojeek, Brave), and implies no API key is needed via 'free engines'. It stops short of noting rate limits, failure modes, or latency, so it is not fully exhaustive.

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?

Four short sentences, front-loaded with the primary instruction and followed by return shape, workflow, and the skip condition. Each sentence adds a distinct decision-relevant fact with no padding.

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?

Although there is no output schema, the description compensates by naming the returned fields, and it covers the full search-then-read workflow relative to its siblings. Nothing an agent needs to invoke it correctly is missing.

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 100%, so the schema already documents both parameters including the 1-20 range and default for max_results. The description adds no query syntax or formatting guidance beyond 'Keep it specific but natural', so the baseline of 3 applies.

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 states a specific verb and resource ('find web pages') plus the concrete return shape (titles, URLs, snippets), and it explicitly contrasts itself with the sibling fetch_page. An agent can distinguish it from fetch_page and fetch_llms_txt without opening any schema.

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?

It gives an explicit ordering rule ('Use this FIRST'), a follow-up workflow ('Then use fetch_page on the best URL'), and an exclusion condition ('If you already have a URL... skip this and go directly to fetch_page'). Nothing about when to choose this vs. alternatives is left to inference.

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