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

With no annotations, the description carries the full burden, and it does disclose the computational method, the observations object shape, and the return contents (posterior percentiles, judgment state, supporting/refuting evidence, reliability). It omits side-effect/permission context and whether any state is mutated, but the methodological and output transparency is substantial.

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 text is compact and front-loaded: the core operation comes first, then the observations format, then the return value. Every sentence carries information, though the parameter block is dense enough to require careful parsing.

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 6-parameter statistical tool with no annotations and no output schema, the description covers the return format and the observations structure but leaves priors, n_samples, and the required pattern argument undocumented. Adequate but with clear 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?

Schema coverage is 0% across 6 parameters, so the description must compensate. It thoroughly documents the observations array (evidence_id, state enum, basis enum, reliability range, note) and partly explains benchmark/stage_gap auto-judgment, but leaves priors, pattern, and n_samples entirely unexplained.

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 gives a specific verb+resource: judging competing hypotheses of a pattern using Bayesian updating plus Monte Carlo. It is precise about the operation and method, but does not name or contrast itself with siblings like next_best_evidence or screen_patterns.

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 implicitly routes the agent: evidence_id is resolved via get_catalog('evidence'), and metric/step-linked evidence is auto-judged through benchmark/stage_gap. However, there is no explicit statement of when to use this tool versus alternatives such as screen_patterns or assess_stage.

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?

No annotations are provided, so the description carries the full burden. It does disclose one meaningful behavioral trait – domain scores are deliberately not averaged (영역 점수를 평균하지 않습니다) – which tells the agent how the judgment is aggregated. It says nothing about whether the call is read-only, whether it persists state, or what the returned judgment looks like.

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?

Purpose is front-loaded in the first clause, followed by compact per-parameter annotation. The dense bracket/enum notation is information-rich but readable. Nothing is obviously padded, though the terse notation demands familiarity.

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 3-parameter judgment tool with no annotations and no output schema, the parameter documentation is solid but the definition omits what the tool returns and where it sits in the workflow relative to siblings. An agent can construct the call but is left guessing about the result and prerequisites.

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 0%, so the description must compensate, and it largely does: it defines b_stage's domain (B1~B4), points b_basis to the judgment rationale, and enumerates the domains item structure with code (D01~D08), o_stage (O1~O4), status, and confidence enum values. It stops short of covering all allowed values or edge cases, but this is well above the schema's own documentation.

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+resource: judging (판정) the consistency (정합성) of growth stage. That is clearly distinct from sibling names like assess_hypotheses or compute_metrics. However, it never names or differentiates itself from related siblings such as assess_hypotheses, so the differentiation is only implicit.

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?

There is no guidance on when to call this tool versus the many siblings (assess_hypotheses, compute_metrics, next_best_evidence). Context must be inferred entirely from the parameter names. The one implied rule ('does not average domain scores') is about computation, not about when to use the 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?

With no annotations, the description carries the full burden and does a fair job: it discloses the internal branching (CatBoost quantile regression + SHAP factors when a trained model exists, otherwise the catalog cohort table) and that a sample-table flag is returned. It does not cover permissions, cost, or error behavior, but the computational behavior is genuinely explained.

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?

Purpose is front-loaded in the first sentence, with fallback behavior and parameter notes following. The parenthetical about the sample-table return flag is slightly extraneous but overall the text is tight with 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?

There is no output schema and no annotations, so the description must do the work; it helpfully names the returned percentiles, position, SHAP factors, and sample-table flag. However, the undocumented cohort parameter and the absence of any when-to-use framing leave meaningful gaps for a 3-parameter 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 0%, so the description must compensate. It adds real meaning for two parameters — metrics (output of compute_metrics) and company_features (industry, revenue, headcount, tenure) — but the cohort parameter is never mentioned, leaving one of three parameters undocumented.

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 (계산합니다 / computes) and resource (peer-cohort expected range P10·P50·P90 and each metric's position), and it ties its input to the sibling compute_metrics. It is clear what the tool produces, though it never explicitly frames itself against the other siblings (assess_stage, screen_patterns), so differentiation is implied rather than stated.

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 context is only implied: the metrics parameter is described as coming from compute_metrics results, which hints at the required upstream step, but there is no explicit 'use this when / not when' or comparison to alternatives. The model-vs-catalog branching is internal behavior, not user-facing guidance.

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?

With no annotations, the description carries the full burden. It does disclose the key side effect (file creation on disk) and points at a specific return field (files.dashboard), which is useful behavior the agent would not otherwise know. However, it says nothing about overwrite behavior, permissions, or failure modes for a tool that writes artifacts.

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 sentences, zero filler, with the purpose front-loaded before the follow-up instruction about relaying the dashboard path. Nothing needs trimming or reordering.

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 10-parameter, 7-required tool with nested objects, no output schema, and no annotations, the description is far too thin. It partially compensates for the missing output schema by naming files.dashboard, but leaves the entire input contract and the 'schema v1.0' expectation unexplained.

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?

All 10 parameters (company, industry, stage, benchmark, hypotheses, next_evidence, actions, output_dir, headline, base_date) have 0% schema description coverage and the description explains none of them. It references a 'schema v1.0' for the output but adds no meaning about the expected input shapes, which is especially damaging given the nested objects and 7 required fields.

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 a specific verb ('만듭니다' / creates) and two concrete artifacts: a diagnostic-result JSON (schema v1.0) and an offline HTML dashboard file. This is clearly distinguishable from sibling computation tools like compute_metrics or save_case, though it never explicitly contrasts itself with 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?

There is no when-to-use guidance, no prerequisite conditions, and no mention of alternatives such as save_case or get_case. The only actionable instruction ('tell the user the files.dashboard path') is a post-invocation courtesy, not usage guidance.

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?

With no annotations, the description carries the full burden, and it does disclose one real behavior: metrics lacking data return '평가 보류' plus a reason rather than failing, which tells the agent to expect partial results. However, it says nothing about whether the call mutates state, what permissions are needed, or how the 15 metrics are selected.

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, no filler, and the core purpose is front-loaded ahead of the argument-format and edge-case details. Every clause carries information.

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 6-parameter tool with no annotations and no output schema, the description is thin: it never names or categorizes the 15 metrics, does not explain the financials object, and gives no date-format convention for the required period bounds.

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 0%, so the description must compensate. It clarifies the three *_csv parameters accept either a file path or raw CSV text, but leaves financials (an opaque object), period_start, and period_end formats completely undefined, covering only half the parameters.

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 concrete verb and resource with scope: computing 15 metrics over a period. It does not differentiate from the sibling benchmark_metrics, which is the obvious alternative for metric computation, 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?

There is no when-to-use guidance, no prerequisites, and no mention of benchmark_metrics as an alternative or of when this tool is preferred over it. Usage is only implied by the required period arguments.

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?

No annotations are provided, so the description carries the full burden. It usefully discloses the return content of both branches (details include results, expert review, and performance), which goes beyond the schema. However, it says nothing about read-only semantics, authentication, or how many items constitute the 'recent case list' or how that list is ordered or paginated.

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?

A single sentence that front-loads the decisive condition (case_id present vs absent) and enumerates the detail payload without waste. Nothing is padded.

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, no-output-schema tool, the description covers both operating modes and the content of the detailed result, which is what an agent needs to call it correctly. Minor gaps remain around list behavior (size, ordering) and the absence of any permission or safety note.

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 0% and the schema only labels the field 'Case Id' with a null default. The description adds real meaning by explaining that the mere presence or absence of case_id switches the tool between detail and list mode, which is not derivable from the schema alone.

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 a specific resource (case) and clearly specifies two distinct behaviors: returning case detail (results, expert review, performance) when case_id is present, and listing recent cases when absent. It distinguishes itself from the obvious sibling save_case by being the read side, though it does not explicitly contrast with other read siblings like get_catalog.

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 implied by the conditional on case_id: pass an ID for detail, omit it for a list. That is useful routing guidance for the two modes, but there is no explicit statement of when to prefer this tool over alternatives such as get_catalog or compute_metrics, and no prerequisites or exclusions are given.

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?

No annotations are provided, so the description carries the full burden; '조회' implies a read-only retrieval, which is useful, and it discloses the catalog's contents and counts. However it does not confirm read-only status, pagination, or return shape, leaving meaningful behavioral gaps for a zero-annotation tool.

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?

Purpose is front-loaded in the first phrase, and the pipe-delimited section list is dense and waste-free. Slightly terse given it omits the default value and the fact that section is optional.

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 single optional parameter with no output schema or annotations, the description adequately tells the agent what values to pass and what each contains. It does not describe what a catalog lookup returns or note that section defaults to 'patterns', which leaves a modest gap.

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 0% schema coverage and no enum, the description is the only place that defines the parameter's valid values: patterns, metrics, evidence, terms, rules. It even attaches meaning and counts to each option (14 patterns/62 hypotheses/8D, 15 metrics, etc.), fully compensating for 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?

States a specific verb+resource ('카탈로그 조회' = catalog lookup) and enumerates the five sections it covers, which effectively separates this reference-data tool from the analytic siblings (compute_metrics, assess_stage, etc.). It does not name any sibling directly, 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?

There is no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives such as get_input_templates or screen_patterns. The section list hints at what can be retrieved but never states the condition under which an agent should call this 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?

No annotations are provided, so the description must carry the behavioral burden. It says the tool returns templates and rules, but does not state read-only behavior, side effects, auth requirements, or the response format.

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 with no filler. Every element adds useful scope information.

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 no-parameter getter, knowing what categories are returned is useful. However, with no output schema, it does not describe the response structure or how the templates/rules are presented, leaving an agent partly blind after 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?

The tool has zero parameters, so there are no parameter semantics to explain. The baseline score for a no-parameter tool 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 names a specific verb/resource: returns input-data templates and writing rules, with concrete examples (roster CSV, finance, decision records, milestones). It is clearly differentiated from sibling action tools, though it does not explicitly compare itself with get_catalog or get_case.

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 states what the tool returns but gives no explicit when-to-use guidance, prerequisites, or alternatives among the many sibling tools. The use case is inferable from the name and content, but not actually guided.

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?

No annotations exist, so the description carries the full burden. It usefully discloses return semantics (ranked evidence, expected information gain, hypothesis probabilities under favorable/unfavorable outcomes) in place of a missing output schema, but it says nothing about required preconditions, whether the call is a pure read, or how results change with partial observations.

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 sentences, no filler, with the core purpose front-loaded and the practical use case trailing as secondary detail. The density is high given the length, though the parenthetical detail about probabilities is arguably more than the selection decision needs.

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?

The description compensates reasonably for the absent output schema by describing what is returned, but against 5 parameters at 0% coverage, no annotations, and a math-heavy domain (information gain, benchmark comparison), it leaves the agent unable to supply inputs or confirm prerequisites. It is not adequate for a tool of this complexity.

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?

Five parameters with 0% schema description coverage, and the description names none of them. '대상 가설' loosely gestures at the `target` field, but `pattern` (required), `benchmark`, `stage_gap`, and `observations` are entirely unexplained in both schema and prose, leaving an agent unable to populate them correctly.

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: it returns un-obtained evidence ranked by impact on hypothesis assessment, and it even names the ranking criterion (expected information gain) and the probability framing. That is far more than a tautology, though it never names which sibling it is not (e.g. assess_hypotheses or recommend_actions), so sibling differentiation must 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?

It gives one concrete downstream use — turning the output into interview questions or a data-request list — which implies the 'value of information collection' context. However, it never states when to call this rather than assess_hypotheses or recommend_actions, nor any prerequisite such as needing existing observations for the calculation to be meaningful.

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?

With no annotations, the description carries the full behavioral burden, and it does disclose one meaningful trait: pre-verification execution tasks are conditionally labeled 'contingent on expert review confirming the cause.' However, it says nothing about whether this persists state, requires auth, or how it relates to record_review/record_outcome, leaving large behavioral gaps.

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 sentences with no filler; the primary action (create candidates + check points) is front-loaded and the conditional-labeling caveat follows. Efficient and well-ordered.

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?

A recommendation-generating tool with no annotations, no output schema, and 3 undocumented parameters needs the description to fill more gaps. It omits what the returned candidates look like, how strengths/next_evidence feed in, and any prerequisite state, so it is under-specified for the agent's needs.

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 0% across 3 parameters, so the description must compensate. It only obliquely explains hypothesis_results ('가설 판정 결과') and gives no meaning at all for strengths or next_evidence, leaving two of three inputs undocumented in both places.

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: generating improvement-task candidates (verification/improvement/build/monitoring/maintenance) plus 3/6/12/24-month check points from hypothesis assessment results. An agent understands the output shape, though it never explicitly contrasts itself with siblings like assess_hypotheses or next_best_evidence.

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 phrase 'based on hypothesis assessment results' implies this runs downstream of hypothesis assessment, giving weak sequencing guidance. There is no explicit when-to-use, when-not-to-use, or named alternative among the many siblings, so the agent must infer its place in the pipeline.

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?

No annotations are provided, so the description carries the full behavioral burden, yet it only implies a write operation. It does not say whether recording an outcome overwrites a prior entry for the same month, what permissions are required, or whether the metric fields are mandatory alongside a verdict.

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, front-loaded with what is recorded before the enum constraint. Nothing is wasted, though for a 7-parameter write tool the terseness edges into under-specification rather than pure efficiency.

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 7 parameters, 0% schema coverage, no annotations and no output schema, the description covers only the enum and the month checkpoints. An agent still lacks the meaning of metric_code/metric_value, the units, and the write semantics needed 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 coverage is 0% and there are 7 parameters, so the description must compensate. It does partially: the allowed values IMPROVED|NO_CHANGE|WORSENED|INCONCLUSIVE are supplied for `result` even though the schema declares it as an unconstrained string, and '3·6·12·24개월' effectively documents the valid `month` values. The remaining parameters (case_id, action_id, metric_code, metric_value, note) are left undocumented.

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 (기록합니다 / records) and a precise resource: the 3/6/12/24-month outcomes of an improvement action, which cleanly distinguishes it from the sibling record_review. It does not explicitly name or contrast with any sibling, 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?

The description implies the tool is used after an improvement action has run long enough to have a 3/6/12/24-month checkpoint, but it never states when to call it vs. record_review, whether the action/case must pre-exist, or what to do at each interval. No exclusions or prerequisites are given.

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?

With no annotations, the description carries the full burden. It does usefully disclose a non-obvious behavior — AI draft and final judgment are stored separately, and records accumulate into future training data — which clarifies the ai_status/final_status split. It says nothing about permissions, overwrite semantics, error behavior, or what the call returns.

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 no filler, and the core action plus the target-example clarification are front-loaded. The density is appropriate, though it spends more words on the training-data rationale than on the undocumented parameters.

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 6-parameter, 5-required mutation tool with no annotations, no output schema, and 0% schema coverage, the description covers only one parameter's semantics. An agent could not reliably populate reviewer, rationale, or final_status from this definition, leaving a substantial gap.

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 0% across 6 parameters, so the description must compensate. It clarifies only 'target' with example value formats (P07-H04, ALIGNMENT, D03); case_id, reviewer, rationale, ai_status, and the critical final_status are left entirely undocumented in both schema and description.

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 a specific verb and resource ('전문가 판단을 기록합니다' — records expert judgment) and gives concrete examples of the target domain (hypothesis IDs, ALIGNMENT, 8D codes), so an agent knows what this writes. It does not distinguish itself from the near-sibling record_outcome, which is a real ambiguity in this tool set.

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?

No when-to-use, when-not-to-use, or alternative-tool guidance is given. The reader must infer from the target examples alone that this is for human/expert verdicts rather than AI-generated ones; nothing routes the agent between this and record_outcome or save_case.

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?

With no annotations, the description carries full burden. It does disclose the mutation (writes to a local case DB) and the return value (case_id), which is genuine behavioral information. It omits permission requirements, overwrite/duplicate handling, and failure behavior for an unannotated write tool.

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?

A single front-loaded sentence with no filler: the action, the source artifact, the destination, and the return value. Efficient, though it could note the ordering dependency more explicitly.

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?

No output schema exists, so the description compensates by stating the case_id return, which is correct. However, for an unannotated write tool with an undocumented parameter it leaves gaps around error cases, destination details, and duplicate handling.

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 single parameter has 0% schema description coverage, so the description must compensate. It clarifies the semantic origin of the path (output produced by build_report), which is useful, but does not specify path format, absolute vs. relative, or file requirements.

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 (save) and resource (local case DB), and names the upstream producer build_report, which lets an agent place it in a workflow. It reads as distinct from the get_case retrieval sibling, though it does not differentiate itself from other write-side tools like record_review or record_outcome.

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 implied: the input is 'the JSON file build_report creates,' which hints at ordering and prerequisites. There is no explicit when-to-use vs. when-not, and no stated alternative for persisting or retrieving cases.

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?

No annotations are provided, so the description carries the full behavioral burden. '찾습니다' (find) hints at a read operation, but nothing confirms read-only status, side effects, auth needs, or whether results are persisted. For a screening tool in a case workflow, this is a meaningful gap.

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 terse lines with the purpose front-loaded and one line per parameter. No filler, and the structure mirrors the input list. Appropriate for a small utility tool.

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 3-parameter tool with 0% schema coverage, no annotations and no output schema, the description covers purpose and input provenance but omits behavior, output expectations, and when-to-use relative to siblings. Adequate but incomplete for the tool's complexity.

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 0%, so the description must compensate, and it does: it maps each of the three parameters to its source (benchmark_metrics result, assess_stage's stage_gap, rules tags) and even gives a sample tag value (management_lag). It stops short of describing expected shapes, but the origin mapping is genuinely useful.

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 (찾습니다/find) and resource (pattern candidates among P01~P14) to be reviewed. An agent can tell this is a screening step distinct from compute_metrics or recommend_actions. The only weakness is that 'P01~P14' is opaque without the catalog, but the core action is 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?

The description wires the inputs to their producing tools (benchmark from benchmark_metrics, stage_gap from assess_stage, tags from rules), which implies where this sits in the pipeline. However, it never states when to call this versus siblings like assess_hypotheses or next_best_evidence, so usage is only inferred.

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