Softician Notary Exam Prep

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

Even with readOnlyHint=true already in annotations, the description adds a critical behavioral detail: the tool returns the question and answer choices WITHOUT the correct answer, requiring a follow-up call to the sibling tool. This goes beyond the annotations and gives the agent essential information about the tool's behavior and limitations.

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 two sentences long, front-loaded with the core purpose, and efficiently conveys both the tool's function and the required follow-up action. Every word earns its place with no redundancy or irrelevant detail.

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 tool's low complexity, the presence of an output schema, and comprehensive annotations, the description fully covers the necessary behavioral and workflow context. It clearly states what is returned and what the agent should do next, leaving no critical gaps.

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 already provides 100% coverage of both parameters, including optionality, examples, and default values. The description does not add any additional parameter semantics beyond what the schema already states, so the baseline score of 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?

The description uses a specific verb ('Fetch') and clearly identifies the resource ('real practice question from Softician Exam Prep's California Notary Public exam question bank'). It also distinguishes itself from the sibling tool by explicitly stating that the correct answer is not included, directing the agent to grade_practice_answer for checking.

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 explicitly explains when to use this tool and what to do next: call grade_practice_answer with the returned questionId to check the response and see the explanation. This clear workflow guidance differentiates the tool from its sibling and provides actionable usage context.

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.