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

Annotations already declare this a read-only, idempotent, non-destructive, closed-world operation, so the safety bar is low. The description adds genuinely useful behavior beyond them: the conversion is based on IRCC equivalency tables and the reported overall level is the minimum of the four abilities, which an agent could not infer from the schema or 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?

Two tight sentences with zero filler, and the conversion rule is front-loaded ahead of the aggregation note. The enumeration of test variants largely duplicates the `test` enum description in the schema, which is the only minor redundancy.

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, so the description should say what comes back, yet it only hints that four per-ability levels plus an overall level exist and never states the return shape or format (e.g. numeric CLB vs a string). Combined with the undocumented input scales, meaningful gaps remain for a tool whose entire value is a precise conversion.

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 only 20%: the `test` enum is documented, but listening/reading/writing/speaking are bare numbers constrained only to 0-999. The description does not tell the agent the actual scale per test (IELTS bands vs CELPIP 0-12 vs PTE 0-90 vs TEF 0-699 vs TCF 0-20), so a caller can easily supply values on the wrong scale and get silently wrong CLB output.

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 opens with a specific verb and resource ("Converts ... ability scores to CLB levels") and enumerates the exact tests covered, so an agent knows precisely what computation happens. It does not reference the sibling tools (calculate_oinp_score, get_scoring_grid), but those serve clearly different purposes, so differentiation is implicit rather than stated.

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 applicable situation is implied: use it when you hold raw language-test scores and need CLB levels for an immigration-related workflow. However, there is no explicit when-to-use, no prerequisites (e.g. which test variants are accepted for which program), and no routing guidance relative to the sibling scoring tools.

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