Softician Notary Exam Prep

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses a key behavioral trait: the correct answer is NOT returned, and it directs the user to the sibling tool for grading. This adds substantial value by explaining the exact behavior and intended follow-up, which annotations alone do not convey.

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 concise: two sentences, front-loaded with the action (Fetch), and each sentence serves a purpose. No redundant filler, and the key behavioral note about the missing answer is clearly included.

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?

Given the output schema exists (which would document return fields), the description sufficiently explains the essential return information (question, 4 answer choices, questionId) and the workflow with the sibling tool. There are no major gaps for a read-only fetch operation with clean annotations.

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 description coverage is 100%, so the schema already documents both parameters clearly. The description does not add extra semantic details beyond what the schema provides, but it does reinforce the examType context by specifying the California Notary Public exam. This is adequate, but no real value is added beyond the schema.

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 clearly states the tool fetches a real practice question from a specific exam question bank, and distinguishes itself from the sibling grade_practice_answer by explicitly noting that the correct answer is omitted. It uses specific verbs and resources, leaving no ambiguity about its function.

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 provides explicit usage guidance: call grade_practice_answer with the returned questionId once you have a response to check the answer and see the explanation. This gives a clear workflow and implicitly contrasts with the sibling tool, making it obvious when to use this tool versus the alternative.

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

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

The annotations already declare readOnlyHint=true and destructiveHint=false, so no contradiction exists. The description adds useful behavioral context by clarifying that the tool returns correctness and the official explanation, and it also notes the dependency on a prior get_sample_question result, which goes beyond the structured metadata.

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 a single, front-loaded sentence that immediately conveys the action, target, and outcome. Every phrase contributes essential information: what to grade, the source of the question, and what will be returned.

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 simple two-parameter grading tool with an output schema, the description is fully adequate. It covers the purpose, source dependency, and expected return value without unnecessary elaboration. The presence of an output schema means detailed return-field documentation is not needed in the description.

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?

The input schema provides 100% parameter coverage: questionId is described as the ID returned by get_sample_question, and response has an enum of A-D. The description adds no additional meaning to these parameters, so per the baseline for high schema coverage, a score of 3 is appropriate.

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 uses a specific verb ('grade') and identifies the exact resource ('response to a practice question') and expected outcome ('return whether it was correct plus the official explanation'). It clearly distinguishes this tool from its sibling get_sample_question, which provides sample questions rather than grading them.

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 gives clear contextual guidance by stating the input must be a response to a question 'previously returned by get_sample_question', which establishes the intended sequence of usage. It does not explicitly state when not to use the tool or name alternative grading tools, but the sibling relationship is evident enough for a focused practice-answer workflow.

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