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

Without annotations, the description carries the full burden. It explains that the tool searches the live web, provides AI-synthesized answers, and automatically picks the answer shape. It also details model behaviors (fast, standard, deep, agent) and the max_steps parameter for the agent mode. However, it does not disclose any side effects, rate limits, authentication needs, or explicitly state that it is read-only.

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 two sentences plus a sentence about parameters. It is front-loaded with the main purpose, and every sentence serves a distinct purpose: stating the core functionality, listing answer shapes and automatic selection, and providing usage guidance. No wasted 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?

Given the tool's complexity (10 answer shapes, multiple models) and the absence of an output schema, the description covers the main use cases well. It explains that the answer shape is selected automatically, which helps users understand they don't need to specify a shape. It could be slightly more specific about how to target specific answer shapes, but overall it provides sufficient context for an AI agent.

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 baseline is 3. The description adds value by explaining the semantic differences between model enum values (e.g., 'pixserp-fast = quick single-search lookup'), providing guidance on query phrasing ('Phrase as a natural-language question or a specific topic'), and clarifying that max_steps is only used when model = pixserp-agent. This goes beyond what the schema 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 it searches the live web and returns an AI-synthesized answer with structured citations. It also lists 10 answer shapes and explains that the correct shape is chosen automatically from the query, making the tool's purpose very specific and distinguishable, even without siblings.

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 says 'Use this whenever you need fresh, factual information from the web,' providing clear when-to-use guidance. It does not mention alternatives or when not to use, but the absence of sibling tools makes this less critical.

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