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

Annotations provide only readOnlyHint=true, so the description carries the behavioral load. It goes beyond that by explaining the three-state output, that it 'never a guess', that it cites specific rules with scores and dates, and that it is 'a case to argue, not a verdict' – making the tool's behavior and limitations explicit. No contradiction with 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?

The description is long but every sentence contributes: it leads with the core question, then specifies the inputs, the three states, the details of covered items, and ends with a crucial caveat about the tool's advisory nature. It is front-loaded and structured with semicolons, though slightly verbose compared to a minimal two-sentence description.

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?

For a tool with 3 parameters, a detailed output schema, and the readOnlyHint annotation, the description is remarkably complete. It explains the tool's domain, the input semantics, the result states, the level of detail in the answer, and the philosophical stance ('not a verdict'). An agent has enough to invoke it correctly without needing to guess about missing behavior.

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 each parameter has a description, so the baseline is 3. The description adds meaning beyond the schema by explaining how 'held' items (exams/courses) are processed against the target school's rules, how the three-state result is derived, and what the answer includes. It doesn't detail every parameter, but it enriches the schema's existing explanations.

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 states a specific verb-resource pair: it evaluates whether prior credits will transfer to a target school, and details the three output states (covered, missing, unknown) and the information returned for covered items. This clearly distinguishes it from siblings like find_comparability or suggest_credit_for_my_learning by naming its specific inputs (AP/CLEP/IB exams or courses) and the use of the target school's published transfer rules.

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 description clearly establishes when to use this tool: when a person wants to know if their held credits will apply at a target school, with a specific scenario described. It does not explicitly name alternatives or exclusion criteria, but the context is unambiguous enough for an agent to select it over sibling tools like suggest_credit_for_my_learning.

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