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

Annotations already cover the safety profile (readOnly, non-destructive, open-world), so the bar is lower, yet the description adds substantial behavior: list-price vs optimized cost is what's tested, over-constrained queries return empty recommendations plus nearMisses flagged as misses, and the ranking is server-side and final. It also enumerates the constraint dimensions, which is not derivable from 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?

Front-loaded with purpose, then routing, then constraints, then failure mode — a good order. It loses a point for redundancy: the 'top 3' return is described twice ('Returns a ranked top 3...' and 'Returns a top 3 as structured facts — efficiency rank, ELO...'), which overlaps with the output schema.

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?

Output schema exists so return fields need not be explained, yet the description still covers the failure path (over-constrained → nearMisses) and the cost basis, leaving no gap an agent needs to call this 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 coverage is 100%, so baseline is 3, but the description adds value by naming the four constraint dimensions (monthly budget, minimum ELO, required capability, self-hostability) that drive the per-constraint satisfied/violated breakdown, and by clarifying that minElo is a hard filter rather than a soft preference.

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?

Opens with a specific verb+resource ('Pick a model') and immediately scopes it: 'ranked top 3 for one workload under optional constraints.' It explicitly names and distinguishes the sibling compare-llm-models, so an agent can route correctly without reading 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 an explicit when-to-use rule with a concrete example ('what should I use for support tickets under $500 a month') and an explicit when-not rule pointing at the alternative tool for browsing/filtering the catalogue.

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