Query 2026 50-Site SaaS AI Truncation & /llms.txt Benchmark

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely new operational context: it is a static benchmark lookup requiring no API key, so no auth or rate-limit surprises. It stops short of describing result shape or pagination, but with annotations carrying the safety burden this is a solid addition.

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 the retrieval purpose, followed by usage and exclusion guidance, so an agent scanning the first clause gets the gist. The long parenthetical of measured metrics is dense but earned, since it explains what the benchmark actually contains. Slightly heavy, but every clause carries information.

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?

With no output schema, the description does useful work by enumerating the measurable fields (token weight, truncation cutoff, price omission, /llms.txt adoption, Lighthouse ARD), which tells the agent what comes back. It does not describe response structure or how the 50-site summary is organized, leaving a small gap for a data-retrieval tool.

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?

Single optional parameter with 100% schema description coverage; the schema already explains that omitting 'domain' returns the full 50-site summary, so baseline 3 applies. The description only loosely reinforces this by referencing peer comparison, adding no syntax, format, or canonical-domain details beyond the schema.

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 (retrieves) and resource (empirical benchmark telemetry from the 2026 50-Site SaaS & E-Commerce AI Truncation Study) and enumerates the measured dimensions, so an agent knows exactly what data lives behind it. It also distinguishes itself from siblings by name (audit_ai_readiness) and scope (study data vs. live scan).

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?

Explicit when-to-use ('comparing a site against industry peers', 'research questions about the Static /llms.txt Paradox') and explicit when-not-to-use ('Do not use this tool to run a live scan on an arbitrary unbenchmarked URL'), with the correct alternative named. This is the full when/when-not/alternative pattern.

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