ComputePrep

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

Goes well beyond the annotations: it discloses that the question is pre-validated to have exactly one correct answer, that every conversation sees the same question, that attempts are counted per conversation rather than per person, and that Hindi is only served while the server language switch is on. These are exactly the behavioral facts an agent needs and none are in structured fields.

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 what it fetches, then return contents, then the negative scope, then the parameters. Dense and mostly waste-free, though the attempt-counting point is stated twice across description and schema.

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 correctly covers the return shape (exam, difficulty, expected time) and explicitly notes what is withheld (answer, explanation). It is slightly incomplete about the actual question text being returned and about what the agent should do with attemptId afterward, but the essentials are present.

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 explains both lang and attemptId, including the per-conversation counting rule. The description's 'Pass lang hi for Hindi' and the attempt-count sentence largely restate the schema rather than adding new semantics.

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 ('Fetch today's ComputePrep drill') and pins the domain to quantitative aptitude for named exams. Siblings (get_drill_hint, get_practice_drill, submit_drill_answer) are clearly different operations, and the description makes clear this one only serves today's single question.

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?

Establishes the context of use ('today's drill', one fixed question per conversation) and excludes a wrong expectation by stating it does not return the answer or worked explanation. It does not explicitly name get_drill_hint or submit_drill_answer as the next step, leaving that routing to inference.

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 description discloses two things annotations cannot: that attempts are consumed per conversation rather than per person, which explains the non-readOnly, non-idempotent hints on a 'get' tool, and that output is deliberately limited. It does not say what happens when attempts run out, but the core behavioral profile is given.

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 short sentences with the capability stated first, then the output restriction, then the accounting rule. No filler, though the attempt-counting sentence duplicates schema text and could be trimmed.

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 carries the burden of describing the return value, and it does so at the right level ('first step', no option or worked explanation). Combined with a fully documented schema, an agent has enough to call it; only the exhaustion/failure behavior is unstated.

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 'drill', 'attemptId', and the 'lang' enum. The description only echoes the drill-type concept and repeats the per-conversation attempt accounting already stated in the attemptId field description, adding no new parameter detail.

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 the resource (a hint / first step for a ComputePrep drill question) and constrains what it returns ('Does not reveal the option or the worked explanation'), which separates it from submit_drill_answer. The phrasing 'Name the drill type and ask for the first step' is slightly imperative rather than declarative, but the purpose is still clear.

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: an agent can infer this is a pre-answer nudge, and the note about not revealing answers hints it should be used before submit_drill_answer. There is no explicit 'use this when...' or named alternative, and no mention of ordering relative to get_daily_drill or get_practice_drill.

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?

Adds real value beyond annotations: it discloses the anonymous quota limit, that questions are excluded from ranking, and that each has a verified single answer. The readOnlyHint=false annotation is consistent with a quota-consuming operation, and the description explains that consumption. Lacks detail on what the response contains or what happens when the quota is exhausted.

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-loads the core action before the constraints and quota. Four compact sentences with little waste, though the quota and ranking notes could be tightened slightly.

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 state-consuming tool with no output schema, the description covers the essentials an agent needs: quota, ranking exclusion, correctness guarantee, and language behavior. Only the return shape and quota-exhausted behavior are left unstated.

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 both lang and attemptId are already fully documented. The description adds only a brief 'Pass lang hi for Hindi' hint, which restates the schema's enum explanation. Baseline 3 is appropriate when the schema carries the parameter semantics.

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 (open one extra practice question) and clarifies scope with 'checked to have one correct answer.' It distinguishes itself from siblings by noting 'not today's shared drill' and 'not in the attempt ranking,' clearly separating it from get_daily_drill. The purpose is concrete but not maximally sharp.

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?

Provides clear usage context: a per-conversation quota ('2 practice questions today') and explicit exclusions ('not today's shared drill,' 'not in the attempt ranking'). It implies when to reach for it over the daily drill but does not name siblings directly as alternatives.

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 description goes well beyond the annotations by explaining that a wrong guess does not reveal the correct option, that a Verified answer and worked explanation appear only when the round finishes, and that correct answers add a practice link. It also clarifies the comparison-line semantics: attempts used rather than time, anonymous, not the public leaderboard, and excluding dev/staging/test conversations.

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 front-loaded with purpose and return behavior, and every section is relevant. It is somewhat long because it carefully disambiguates the shared-drill comparison line, but it remains readable and free of filler.

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?

There is no output schema, so the description must carry the return-value semantics, and it does: right/wrong, attempts remaining, round-completion answer and explanation, comparison line, anonymity, environment exclusions, and the practice link. It also states the three-attempt limit, which is essential for correct invocation.

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 all four parameters thoroughly. The description reinforces that only one option id is submitted and that attempts are per conversation, but it adds little parameter-specific meaning beyond what the schema already provides.

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 and resource: submit one option id for a ComputePrep drill, and immediately says it grades correctness and reports remaining attempts. This clearly distinguishes it from the sibling retrieval tools like get_daily_drill and get_practice_drill.

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 makes the usage context clear enough: submit an answer during a drill and note the three-attempt limit per conversation. However, it does not explicitly say when to use this versus the sibling get tools, such as calling get_daily_drill first or get_practice_drill for practice questions, so the routing guidance is only implied.

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