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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), so the description's real contribution is data provenance and coverage limits: sourced from College Board 2024/2025 PDFs and the 2026 distributions page, with mean/SD only 'where College Board published them'. That gap disclosure is useful behavior an agent cannot get from annotations. Return format details remain implicit.

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 core return statement, then coverage notes. Three sentences, mostly earning their place; the 'newer than most model training data' clause is slightly promotional but conveys a real freshness caveat.

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 the work of describing the payload (percentages, student counts, mean, SD) and the year coverage, which is adequate for a simple two-parameter read tool. It stops short of stating what happens for exams/years with no published data, which is the one gap an agent might hit.

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% and the schema already documents the short-name examples for 'exam' and the 2024/2025/2026 availability plus default of 2026 for 'year'. The description adds little parameter-level meaning beyond restating that both inputs exist, so the baseline 3 applies.

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: 'Returns the College Board score distribution ... for an AP exam and year', and enumerates what is included (5/4/3/2/1 percentages, 3-or-higher, counts, mean, SD). It is clearly a single-exam retrieval tool, but it never names or contrasts the siblings (compare_ap_exams, estimate_ap_percentile), so differentiation must be inferred.

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?

Usage is only implied: the note that 2026 results are 'newer than most model training data' hints that this is the tool for current-year figures, and the year range is stated. There is no explicit when-to-use/when-not guidance and no pointer to the sibling tools for percentile estimation or exam comparison.

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