Read a page as text

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

Annotations already declare readOnlyHint=true, so safety is covered. The description adds that output is raw plain text for the agent's own reasoning and that the alternative depends on a model key, but says nothing about truncation behavior, page-load state, or what happens on non-text pages.

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 short sentences, purpose front-loaded, the routing rule following immediately. No filler and every sentence earns its place.

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 read-only text-extraction tool with annotations carrying the safety profile and no output schema, the description adequately answers what it returns and when to pick it. Only the tabId semantics and load/truncation behavior are left implicit.

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 50%: maxChars is documented in the schema (default 6000) but tabId is undocumented in both schema and description. The description adds no parameter meaning and does not compensate for the uncovered tabId parameter.

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?

States a specific verb+resource (return page as plain text) and explicitly contrasts itself with the sibling browser_ask, so an agent can distinguish the two without opening either 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?

Gives explicit when/when-not routing: use browser_ask when you need one fact via a model, use this tool when you genuinely need the content to reason over. The selection condition is unambiguous.

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