Estimate an AP score from practice exam raw points

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so safety is covered. The description adds real behavioral context beyond them: the data provenance (College Board's printed scoring worksheet and conversion chart), the guarantee that cut scores are never invented, and the error behavior for unsupported exams. It only omits how the error/edge cases look beyond the one mention.

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 sentences, front-loaded with the action, then the supported-exam whitelist, then the anti-hallucination guarantee. The exam list is long but each item is load-bearing for correct invocation.

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 describe the return values (a weighted composite and an estimated AP score), which is the key gap it needed to fill. Given only 3 parameters and closed-world reads, this is nearly complete, though it could say more about the shape of the returned composite.

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 a baseline of 3 is warranted, but the description adds meaning by explaining that MC and FR inputs are combined into a weighted composite, clarifying what the free-response array is for. It does not clarify per-exam input ranges beyond what the schema already documents.

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 transformation: multiple-choice correct answers and free-response points become a weighted composite and an estimated AP score. This is clearly distinct from the sibling tools (compare_ap_exams, estimate_ap_percentile, get_ap_score_distribution, list_ap_exams), none of which compute a score from raw responses.

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 unusually explicit when-not clause: only the 7 listed exams are supported and 'other exams return an error', with each supported exam and year enumerated. The missing piece is a pointer to a sibling (e.g. list_ap_exams) as the way to discover supported exams, so it stops just short of naming alternatives.

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