spraay_search_web

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

Annotations already declare readOnlyHint and openWorldHint. The description adds value by disclosing the cost ($0.01 USDC), the return type (extracted content + AI answer), and optional configuration. No contradictions with annotations.

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?

Three concise sentences pack a lot of information: main purpose, key features, and cost. No redundancy or unnecessary detail. Front-loaded with the verb 'search' and the resource 'web'.

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 complexity of a web search tool with multiple parameters and optional output schema, the description covers the essential aspects: what it returns, supported features, and cost. It lacks details about pagination or result formatting, but the output schema likely fills that gap.

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 coverage is 100%, so the schema itself documents parameters well. The description adds meaningful context: examples for query, explains enum values for topic and search_depth, and shows usage patterns for include/exclude_domains with array examples.

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 searches the web via Tavily, returns extracted content and an AI-generated answer, and lists features like depth, filtering, and topic focus. It distinguishes from sibling tools like spraay_search_extract and spraay_search_qna by emphasizing web search with enrichment.

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 explains when to use: when you need LLM-ready web results with content extraction and AI answers. It mentions supported features (depth, domain filters, topics) but does not explicitly contrast with alternatives like spraay_search_extract or spraay_search_qna, leaving some ambiguity.

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