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

Annotations already declare readOnly/idempotent/openWorld, and the description adds substantial context beyond them: a SearXNG-first provider chain with automatic Serper/Tavily/bing_rss fallbacks, a 10-minute cache that still bills as a call, and a metering model (100 free calls/day per IP, then 1 credit via x402, no API key, 402 carries accepts[]). These are exactly the operational traits an agent cannot infer from structured fields.

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?

Front-loaded with purpose, then when-to-use, then exclusions, then operational details in a predictable order. Dense and mostly waste-free, though the returns-shape block and the max-count sentence sit slightly awkwardly after the metering discussion.

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?

There is no output schema, and the description compensates by spelling out the response object, including provider, cached and took_ms fields. Caching, metering, fallback behavior and result limits are all covered, leaving nothing an agent needs in order to call it correctly.

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 every parameter (lang, site, count, query, freshness) is already documented in the schema. The description's only parameter remark, 'max count is 10', restates the schema's 1-10 range and adds nothing new. Baseline 3 applies when the schema does the heavy lifting.

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 and resource (search the live web) plus the exact return shape (title, URL, snippet, published date). It explicitly distinguishes itself from the sibling tools an agent might confuse it with: web_fetch, the on-chain tools, and cyberlibrary_fulltext_search.

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?

Provides concrete use-case examples in the agent's own voice ('recent Zcash governance proposals'), an explicit ordering rule ('use it before web_fetch, then fetch the one URL that looks authoritative'), and a 'Do not use' section that routes to named alternatives with the condition that selects them.

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