CertArc

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

Annotations already declare readOnly/idempotent/non-destructive, and the description adds two things they don't: "Does not record anything" (no persistence side effect) and the full return payload, which matters because there is no output schema. Not a 5 because it never explains whether submitting `confidence` changes subsequent behavior or how the blind-spot signal is consumed.

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, front-loaded with the invocation trigger, then the return contract. "A confident wrong answer is a blind spot" is the only sentence with questionable necessity, though it does hint at the confidence parameter's purpose.

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?

Return values and non-persistence are covered, which is the important gap given no output schema, and the read-only annotations carry the safety profile. However, for a 4-parameter tool with two undocumented parameters and no output schema, the description should still clarify what `confidence` and `questionId` mean and how they are used.

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 only 50%: `answer` and `program` are documented in the schema, but `questionId` has no description and `confidence` carries only an enum. The description never mentions any parameter, offering at most a cryptic nudge ("A confident wrong answer is a blind spot") without explaining that confidence is an optional input or what it affects.

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?

Specific verb and resource ("Check practice answer" for "a CertArc practice question") plus a stated return payload: correctness, correct option, and per-option explanations. The trigger condition ("the learner has chosen an answer") implicitly separates it from practice_question, but no sibling is named, so the differentiation is inferential rather than explicit.

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?

"Use this when the learner has chosen an answer" gives a clear, unambiguous invocation condition. It stops short of naming alternatives or exclusions (e.g. use practice_question to fetch a question, get_result_review for post-hoc review), leaving the boundary for the agent to infer.

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?

Annotations already cover read-only, idempotent, non-destructive, and closed-world behavior. The description adds useful context beyond annotations: no account is needed, the response includes domains and weights, public guide links, and a free practice set where available. It does not describe error behavior, but that is a minor gap for this simple lookup.

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 four short sentences with no waste. The trigger condition is front-loaded, followed by access, return, and exclusion information in a logical order.

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 one-parameter read-only lookup, the description is complete enough: it explains the trigger, the scope, the no-account requirement, and what the tool returns. With annotations and a fully described enum, an agent has what it needs to call this correctly.

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 single required program parameter is already well documented. The description lists the same certification names as the enum, adding little semantic detail beyond the schema. The baseline 3 is appropriate when the schema does the heavy lifting.

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 resource and action: returning exam domains and weights for named certifications. It distinguishes itself from sibling tools by explicitly saying it is not for personal progress, which separates it from readiness, study plan, and results tools.

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?

It gives a clear triggering condition: use when someone asks what a listed exam covers. It also gives a useful exclusion: not for personal progress. It does not name a specific alternative sibling tool, so it falls short of a 5.

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 read-only, idempotent, non-destructive, and closed-world behavior. The description adds useful context that the calendar may recommend another action and that this tool does not start or complete a session, which goes beyond the annotations without contradicting them.

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 compact sentences and is front-loaded with the usage condition. Every clause adds useful information: the trigger, the return value, the calendar caveat, and the explicit non-action boundary.

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 read-only tool with one optional, fully documented parameter and rich annotations, the description covers the trigger, return value, and boundary behavior. No output schema is present, but the described return is sufficient for an agent to understand what to expect.

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 schema has 100% description coverage and fully documents the optional program enum, including the default behavior when omitted. The description does not add further parameter semantics beyond what the schema already provides, so the baseline 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 states a specific action: returning the next eligible study sprint and a link to its domain. It is clear what the tool does, though it does not explicitly distinguish itself from sibling study-plan or readiness tools.

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?

It gives a clear trigger: use when the connected learner asks what to study next. It also sets a boundary by saying it does not start or complete a session, but it only implicitly contrasts with the calendar recommendation rather than naming an alternative sibling tool.

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?

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds genuine context beyond that: it discloses the returned components, notes the weekly trend is only present 'when CertArc has recorded it,' and clarifies the interpretive caveat 'A study measure, not an exam prediction' — useful framing that prevents misuse.

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 trigger condition is front-loaded in the first sentence, followed by what is returned and a closing caveat. The middle sentence is dense with enumerated components but every clause carries distinct information; only minor tightening would improve it.

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 carries the burden of describing returns — and it does so thoroughly, naming each component of the readiness payload. Combined with the study-measure caveat and the default-parameter behavior in the schema, an agent has everything needed to call and interpret this tool.

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?

With 100% schema description coverage, the single `program` parameter is already fully documented including its omit-for-default behavior and the five enum values. The description doesn't restate or extend parameter meaning, which is acceptable given the schema does the work, but it adds nothing extra.

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 resource (CertArc's Readiness Score) and enumerates its components: score parts, points within reach, points still ahead, improvement steps with links, and weekly trend. This is clearly distinguishable from siblings like get_results or get_study_summary, which deal with raw results and aggregate study data rather than readiness scoring.

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 opening sentence gives explicit trigger conditions tied to learner intents: asking about the readiness score, how it's built, how to raise it, or which sprint/exam to retake for the most points. It stops short of naming alternatives or stating when not to use it (e.g., 'for individual question results, use get_result_review'), so it doesn't reach the top of the scale.

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 cover read-only, idempotent, non-destructive, closed-world behavior. The description usefully adds that results are answer facts only, exclude question text and answers, include a link to the full review, and follow CertArc result-page decisions for retake availability. It does not discuss auth or rate limits, but those are not central for this read operation.

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 the use case, but it is a long, dense run-on sentence that packs many lists and clauses together. The content is relevant, yet the structure could be clearer and more concise for easier scanning.

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 explaining return content, and it does so thoroughly: incorrect answers, confidence, miss reasons, domains, knowledge areas, time per answer, aggregate breakdowns, retake movement, retake availability, and remaining review. This is sufficient for an agent to understand what the tool provides.

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 67%, with module and program well documented. The description adds useful semantics for the undocumented take parameter, including that it is optional and defaults to latest, and reinforces that module should be taken from the learner's results.

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 retrieval purpose: showing what happened in one sprint or exam, including answer-level facts, breakdowns, retake movement, and review status. It distinguishes this from a simple results list by emphasizing detailed review content and limitations such as 'Answer facts only, never question text or answers.'

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?

It gives an explicit usage condition: 'Use this when the connected learner asks what happened in one sprint or exam.' It also explains the module and optional take inputs, including the default latest take. It does not name sibling alternatives, but the context for when to use it is clear.

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?

Annotations already establish it as a safe read (readOnlyHint=true, destructiveHint=false, idempotentHint=true), so the bar is lower. The description adds real value beyond them: it enumerates the returned sections, clarifies that in-progress sessions come with answered counts, and crucially draws a scope boundary with 'Scores only; never questions.'

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 trigger condition is front-loaded in the first sentence, and the single long second sentence is packed with meaningful payload detail rather than filler. It is dense but every clause enumerates distinct returned data, so little is wasted.

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 burden – and it does so thoroughly, covering every section an agent would receive (per-exam scores, composite, retake movement, mastery, in-progress sessions, Final Readiness unlock state). Nothing needed to call or interpret it correctly appears missing.

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% and the sole `program` parameter carries a full enum plus a clear default-omission explanation, so the schema does the semantic work. The description never mentions the parameter, so it adds nothing beyond it – baseline 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 states a specific verb-plus-resource – it returns completed sprints, practice exams, baseline and final assessment results with named fields (Take 1/Take 2 scores, composite, movement, mastery, in-progress sessions, unlock requirements). It is detailed enough that an agent can distinguish it from sibling reads like get_readiness or get_result_review.

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?

It gives an explicit trigger: 'Use this when the connected learner asks how they did on sprints, practice exams, the baseline or the final assessment.' That is a clear condition for selection. It stops short of naming the sibling alternative (e.g. get_result_review for item-level detail) or stating when not to use it.

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?

Annotations already cover read-only, idempotent, closed-world and non-destructive behavior, so the safety profile is handled. The description adds substantive context beyond that: results are ordered weakest-domain-first, the confidence/result cross-tab is included, and the tool deliberately excludes question content and pass predictions, which sets expectations about what is and is not returned.

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?

Usage triggers are front-loaded in the first sentence, with return content following, so the structure is sensible. The second sentence is a long run-on enumeration of return fields that is dense but each item is meaningful for an agent deciding whether this tool answers the question.

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 burden, and it does: it enumerates the diagnostic sections (blind spots, knowledge-area results, confidence matrix, miss reasons, pace metrics, practice activity) in enough detail for an agent to know whether the response will answer the learner's question. The single optional parameter and its default are already covered by the schema.

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 single 'program' parameter is fully documented in the schema, including the omit-for-most-recent default and the enum of certifications. The description only adds the scoping phrase 'for one certification', which reinforces but does not extend the schema, so the baseline 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 names the specific diagnostic content returned: weakest domains, blind spots, confidence-vs-correctness matrix, tagged miss reasons, pace/timing and practice activity, scoped to one certification. It implicitly separates itself from readiness tools by declaring 'Study signals, not a pass prediction' and 'never question content', though it never names the actual sibling (get_readiness, get_results, get_study_summary).

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?

Explicit trigger conditions are given in natural-language form: use it when the learner asks why they miss questions, where their blind spots are, whether confidence matches results, which domains are weakest, or about pace/timing. It also states a negative boundary ('not a pass prediction'), but it does not name an alternative tool to route to instead, so the when-not guidance is incomplete.

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?

Annotations already declare readOnly/idempotent/non-destructive, and the description usefully confirms "Does not change the plan" in domain terms. It adds genuinely new behavioral detail — dates are in the learner's plan time zone and the exact status vocabulary returned — though it says nothing about auth or what happens when no plan exists.

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 trigger condition, then the payload summary; the status enumeration and return list are dense but each item is informative. Slightly long as a single block, but no filler sentences.

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 annotations gap and no output schema, the description carries the return-shape burden itself and does so well — statuses, next session, week view, counts, plan start, exam date, time zone. Only the empty/no-plan state is unaddressed.

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's own program field already explains the omit-for-most-recent default and that results name their certification. The description adds nothing about the program parameter, so per the high-coverage baseline this is a 3.

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?

Specific verb+resource: reads the learner's CertArc study calendar and enumerates exactly what comes back (dated entries with statuses, next session, this week, overdue, counts, plan start, exam date). The detailed return scope implicitly separates it from get_study_summary and get_next_study_action, but no sibling is named, so it stops short of a 5.

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?

"Use this when the connected learner asks what to study today or this week, what is overdue, or what is on their CertArc calendar" gives explicit trigger conditions an agent can match against a user utterance. It does not state when NOT to use it or point to get_study_summary/get_next_study_action 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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so safety is covered. The description adds real behavioral value by enumerating the payload (progress, exam date, days remaining, access tier and end day) and by naming scope boundaries that annotations do not capture. It stops short of edge cases such as a learner with no recorded activity or how access tiers are derived.

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, front-loaded with the activation condition, then payload, then exclusions. No filler, no repetition of the title, and the highest-value constraints are stated last as a hard stop.

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, and the description compensates by enumerating the return contents precisely, including the access tier values (Paid, Trial, None) and the end date. With a single fully-described optional enum parameter and read-only annotations, nothing needed to invoke it correctly is missing.

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 description for `program` already explains the omit-for-most-recent default and the multi-certification readout. The description only restates "for one certification," adding no format or precedence detail beyond the schema. Baseline 3 is appropriate when the schema carries parameter meaning.

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 resource and scope: aggregate sprint progress, target exam date, days-to-exam, and access tier for one certification. That content set clearly separates it from siblings like get_readiness or get_results, though no sibling is named explicitly. An agent knows what it will learn, but differentiation relies on inference from the returned fields.

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?

Gives an explicit trigger ("when the connected learner asks about their CertArc progress") plus two hard exclusions: never prices or payments, never for another person. These boundaries are exactly the kind of disambiguation an agent needs to avoid drifting into billing or cross-learner queries. Alternatives are not named, but the when/when-not pair is unambiguous.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so 'Read-only' is redundant restatement. The description does add non-annotation context: the read is scoped to the connected learner and explicitly not for another learner, which is a meaningful authorization boundary.

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?

Two tight sentences: the positive trigger first, the exclusions second. No filler, no repetition of the title, and the scope ('one certification') is front-loaded before the caveats.

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, but the description enumerates the returned content (progress, saved schedule, next study option), which is what an agent needs to decide to call it. It does not describe the response shape or how the 'next study option' is chosen, a minor residual gap for a zero-required-param tool.

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 single optional enum parameter is fully documented in the schema, including the omit-default and that every result names its certification and lists others. The description only echoes 'for one certification' and adds no syntax or default behavior beyond the schema, so 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?

Names a specific verb+resource ('see their CertArc study overview') and enumerates the contents: progress, saved schedule, next study option, scoped to one certification. It does not name the overlapping siblings (get_study_summary, get_study_plan, get_next_study_action), so the composite-vs-single-purpose distinction has to 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?

'Use this when the connected learner wants to see their CertArc study overview' gives a clear trigger, and the negative clause rules out another learner, exam answers, and changing plans. It stops short of routing to alternatives for plan detail or next-action specifics, which would be needed for a 5 given the crowded sibling set.

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?

Annotations already cover safety (readOnly, idempotent, non-destructive, closed world), and the description adds genuinely new context: no account required and the deliberate withholding of the answer, which governs the agent's conversation flow. It stops short of describing set size or what happens when 'position' runs past the end.

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 usage trigger is front-loaded and every sentence carries information (triggers, programs, access model, return shape, exclusion). It is slightly dense and could be split, but there is no 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?

With no output schema, the description takes on the return contract and does so: the scenario, options, and a question ID, explicitly not the answer. The main residual gap is behavior at the boundaries of the question set, which matters for a position-indexed tool.

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%, with the program enum and position semantics already documented in the schema. The description adds no syntax or format detail beyond that, 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 one scenario question from CertArc's free public practice set') and enumerates the supported programs, so an agent can match it instantly. It is clearly differentiated from siblings like check_practice_answer by explicitly noting the answer is never returned here.

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?

Gives concrete trigger phrases ('quiz', 'test me', practice/sample exam questions) plus an explicit exclusion ('Not for real exam questions'). It also routes the answer-checking flow to the complementary step ('The answer ... come only from checking the learner's chosen option'), which corresponds to check_practice_answer.

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?

Annotations already declare readOnly, idempotent, and closed-world, so the safety profile is covered. The description adds genuinely non-obvious behavior: the result set is limited to one certification, defaults to the learner's latest when the account is linked, and never surfaces exam questions or answer keys.

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?

Four short sentences with zero filler; the trigger condition leads, followed by return shape, then the negative boundaries. Nothing repeats the title or the annotations.

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 return values and does so ('published guide titles and links'). Combined with the scope and exclusion rules, an agent has everything needed to call it correctly.

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 baseline is 3, but the description adds real meaning by explaining that only one certification's guides are returned and that the learner's latest program is used when the account is linked — context the enum alone does not convey. It does not describe the query matching semantics, which the schema already covers.

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 ('search... study guides', 'returns published guide titles and links') plus the scope: one certification, defaulting to the learner's latest. This clearly separates it from practice_question and get_result_review siblings that deal with questions/results.

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?

'Use this when someone asks for CertArc study guides on a topic' gives a clear trigger, and 'Not a general web search' plus 'Never returns exam questions or answer keys' carve out the when-not case. It stops short of naming the specific sibling tool an agent should use instead for exam questions, so the routing is implied rather than explicit.

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