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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds real value by disclosing the output shape (side-by-side KPIs, % change, improved/worse/flat labels) and the default baseline behavior, though it says nothing about volume, pagination, or what happens with 'all'.

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 with zero filler; the comparison semantics come first and the default behavior second. Nothing is repeated or 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?

With no output schema, the description carries the return-value burden and does so adequately by naming the output contents. For a three-optional-parameter read tool this is nearly sufficient, with only minor gaps around result volume and edge cases like the 'all' period.

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 each parameter is documented in the schema, so the baseline is 3. The description's only addition is the 'e.g. this quarter vs. last' illustration of baseline derivation, which largely restates the schema's own default note.

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 (every KPI side by side) and the transformation applied (% change with improved/worse/flat), which is enough to distinguish it from a single-series trend tool like get_kpi_trend. It stops short of naming any sibling explicitly, so the differentiation is inferred 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?

The description explains the default comparison behavior ('the chosen period vs. the equal-length period before it') and gives an example, which implies when this tool is appropriate. It never states when to prefer it over get_kpi_trend or get_dora_metrics, nor any exclusions, so usage is only implied.

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 disclose the write nature (readOnlyHint=false), non-destructive and closed-world scope. The description adds real context beyond them: the artifact is self-contained HTML, it lands in the output folder, and the tool returns the file path. It omits what happens on filename collision (overwrite vs error) and whether the directory is created if absent.

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?

One sentence, front-loaded with the action and artifact, then the output location and return value. The parenthetical list is dense but each item is a genuine dashboard section, so nothing 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?

With no output schema, the description correctly carries the burden of describing the return value (the file path) and the artifact's contents. The remaining gap is file-handling semantics for `filename` — overwrite behavior and path resolution — which an agent would want before calling.

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%: `period` is fully documented in the schema with its accepted values and default, but `filename` has only a regex pattern and no prose. The description says nothing about either parameter — no default period behavior, no note on what happens if the filename is omitted or already exists — so it fails to compensate for the gap.

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?

A specific verb (Write) plus a concrete resource (a self-contained HTML dashboard) with the exact contents enumerated: KPI tiles, wins/risks, MTTR, change failure, defect trends, DORA tiers, SLA breaches. The output destination (output folder) and return value (its path) are both stated, which cleanly separates it from the read-only sibling getters and from generate_executive_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?

Usage is only implied: the phrase 'leadership dashboard' and the write-to-file behavior suggest this is the reporting/export path rather than a data-retrieval call. It never states when to choose it over generate_executive_summary or compare_periods, nor any prerequisite such as data sources being configured.

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=true and openWorldHint=false, so the safety profile is covered; the description adds genuinely new information about the artifact produced (one-page, markdown, fixed section list) — important because no output schema exists. It omits any note on generation cost/latency or whether missing data degrades sections, which keeps it below a 5.

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 deliverable and its contents front-loaded before the distribution note. Every clause carries information an agent would otherwise have to guess.

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 return-value burden and does so by enumerating report sections and format. Combined with the well-documented `period` enum, an agent has enough to invoke it correctly; the only gap is the undefined `baseline` parameter.

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 `period` parameter is fully enumerated in the schema, but `baseline` has no schema description (50% coverage). The phrase "vs. the previous period" hints at what the comparison baseline means, but the description never explains how `baseline` relates to `period` or what the default comparison is, so it only partially compensates for the coverage gap.

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 concrete deliverable — a one-page markdown quality report for leadership — and enumerates its sections (bottom line, KPI scorecard vs. prior period, DORA tiers, wins, risks, asks). That is far more specific than a restated name and lets an agent distinguish it from raw-metric siblings like get_dora_metrics or get_kpi_trend. It stops short of naming the closest sibling (export_leadership_dashboard) to draw an explicit boundary.

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?

"For leadership" and "Ready for email, Slack or Confluence" imply the audience and delivery context, so an agent can infer this is the polished roll-up rather than a data-fetch call. There is no explicit when-to-use/when-not guidance and no mention of when to prefer compare_periods or export_leadership_dashboard instead, leaving the 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful analytical context by enumerating rates, root causes, and the hotspot threshold of 2x the overall rate, but it does not describe return shape, pagination, or data freshness.

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 phrase with no filler. Every element—services, rates, root causes, and hotspots—adds meaning without repetition.

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 analytical tool with full schema coverage and safety annotations, the description is largely complete: it explains what the breakdown contains and defines hotspots. It could say more about output grouping or how services are ordered, but no output schema exists to carry that burden.

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% for the single period parameter, and the schema documents all accepted formats and the default. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific analytical resource: a breakdown of which services' deployments cause incidents or rollbacks, including rates, root causes, and hotspots. It is clearly distinct from generic DORA or MTTR tools, though it does not explicitly name any sibling alternative.

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 an analytical use case but gives no explicit when-to-use guidance, prerequisites, or exclusions versus siblings like get_dora_metrics or get_mttr. It is left to the agent to infer when this breakdown is preferable.

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=true and openWorldHint=false, so safety is covered; the description adds real value beyond that by disclosing the SLA policy thresholds, the "worst first" ordering, and that only open defects are included. It does not say whether the SLA-breach list is truncated or paginated, which matters given "every defect past its SLA" could be a large result.

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 zero filler: scope, three output components, SLA thresholds, and sort order are all packed into one clause chain. Every element earns its place and nothing is repeated from the schema or 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 returns and does so adequately by naming the three result sections and the ordering. The remaining gap is bucket definition (what the age buckets actually are) and result-size behavior, which an agent would want to know before calling.

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 50%: component carries its own description and priority is a self-explanatory four-value enum, so the schema does most of the work. The description never mentions filtering at all, leaving the agent to infer from "open count by priority" that the priority parameter scopes results, which is a weak but non-zero contribution.

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 scope: currently open defects, broken into age buckets, counts by priority, and an SLA-breach list with the exact thresholds (Critical 2d, High 7d, Medium 30d, Low 90d). That is far more concrete than the sibling get_defect_metrics, but the description never explicitly contrasts itself with that sibling or get_mttr, so differentiation is left to inference.

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?

"Open defects right now" implies a point-in-time snapshot rather than a historical trend, which is a usable usage cue against trend-oriented siblings like get_kpi_trend. However, no alternative tool is named and no when-not-to-use condition is given, so the guidance remains 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.

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuine value by defining escape rate as 'share found in production', which is a semantic clarification not available in structured fields. It says nothing about aggregation level, whether results are current or cached, or how missing components are handled — acceptable gaps given the annotation coverage.

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 with no filler, and the metric inventory is front-loaded. It reads slightly as an output dump rather than a purpose statement, but nothing in it 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?

With no output schema, the description carries the burden of telling the agent what comes back, and the metric list does that well; the schema fully covers the two inputs. What is missing is whether numbers are point-in-time snapshots vs. trends and how they relate to get_kpi_trend or compare_periods, which keeps it short of a 5.

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%: both 'period' (with its full enum of windows plus the last_90_days default) and 'component' are documented in-schema. The description adds no parameter-level detail at all, so the baseline 3 for a fully documented schema is the right score.

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?

Enumerates the specific measures returned (created/resolved counts, escape rate, reopen rate, per-component escape rate, resolution time and SLA compliance by priority), which is materially more informative than the title 'Defect metrics'. It implicitly separates itself from siblings like get_defect_aging and get_mttr by covering breadth rather than one dimension, though it never names those alternatives explicitly.

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 contains no when-to-use or when-not-to-use guidance and never references a sibling. An agent cannot tell from this text whether to call get_defect_metrics, get_defect_aging, or get_mttr when asked for 'defect health' — it has to infer from the metric list.

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=true and openWorldHint=false, so safety and scope are covered. The description's added value is the return content (four keys plus tiering), which is genuinely useful given there is no output schema. It says nothing about defaults, filtering behavior, or how tiers are computed.

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 zero padding, and the most important information (the four keys) comes first. It is a fragment rather than a sentence with a verb, which slightly weakens the framing but costs no space.

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 two-parameter, zero-required, read-only metric tool with full schema coverage and annotations, the description covers the essential question of what the caller gets back. Only the default time window and the meaning of the tier boundaries are left implicit, both minor for this complexity level.

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%, including the full period enumeration and the last_90_days default, so the schema carries parameter meaning on its own. The description adds nothing about period or service, which is the baseline expectation when the schema is this complete.

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 resource (DORA metrics) and enumerates exactly what comes back: the four keys plus an Elite/High/Medium/Low tier per key and overall. That is far more concrete than a restated title. It stops short of distinguishing itself from siblings such as get_mttr or get_change_failure_breakdown, which cover overlapping sub-metrics.

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 statement of when to call this tool versus the many adjacent metric tools (get_kpi_trend, compare_periods, get_change_failure_breakdown). No prerequisites, no mention of the default period, and no exclusions are offered, so the agent must infer selection from the name alone.

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=true and openWorldHint=false, so safety is covered. The description adds the meaningful trait of monthly granularity for a single metric, but says nothing about default period behavior, how far back the series goes, or what a 'series' contains.

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 and short, which is good, but the entire second sentence is a verbatim copy of the schema enum — pure redundancy that occupies most of the description without adding 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?

No output schema exists, so the description carries some burden for explaining what comes back; 'month-by-month series' gives a hint but not the shape (list of periods? values?). With only three simple params and read-only annotations, it is minimally viable but leaves 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 67% and the description's metric list merely duplicates the enum already present in the schema, adding no new meaning. The 'one KPI' phrasing reinforces single-metric selection, but period and service semantics are left entirely to 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: a month-by-month time series for a single KPI. The scope ('one KPI', monthly granularity) is clear, though it never names which sibling to prefer (get_mttr, get_dora_metrics) for the same underlying metrics.

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 guidance and no mention of alternatives, despite siblings like get_mttr and get_dora_metrics covering overlapping metrics. The agent must infer that this tool is for trending a metric over time rather than a point-in-time snapshot.

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=true and openWorldHint=false, so the safety profile is covered. The description adds real behavioral value by disclosing what is computed (mean, median, p90, customer-detected share) and that severity is the default grouping — the latter appears nowhere in the schema. It stops short of explaining metric definitions or empty-period behavior, which keeps it below a 5.

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 first defines the payload and its statistics, the second the grouping options. Metric names are front-loaded and 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 appropriately summarizes the return content (three metric families plus medians, p90 and customer-detected share), and the schema carries period and filter formats. Only the semantics of what MTTR/MTTA/MTTD measure in this system is left unstated, a minor but real 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 coverage is a strong 75%, so the burden is light. The description still contributes the default grouping dimension ('severity (default)'), which the schema does not declare for group_by, and enumerates the same four group_by values, confirming the enum's 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?

The description names the specific resource (incident response metrics), expands every acronym (MTTR/MTTA/MTTD), and enumerates the statistics returned plus the grouping dimensions. This clearly separates it from sibling metric tools like get_dora_metrics or get_defect_metrics, which cover different domains.

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 metric names and the grouping sentence, but there is no explicit guidance on when to choose this over get_dora_metrics, get_kpi_trend or compare_periods, and no stated prerequisites or exclusions. An agent can infer intent from the domain, but must reason about sibling selection itself.

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=true and openWorldHint=false, so the safety profile is covered; the description adds real behavioral value by disclosing the exact payload and, notably, that the reported as-of date governs all calculations. It stops short of stating whether the listed set is fixed or reflects the caller's tenant scope.

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 clause followed by an efficient colon-list of returned fields; nothing is padded. It is a fragment rather than a sentence with a verb, but it earns its length.

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 item by item, including the important as-of date caveat. For a zero-parameter read-only discovery tool this is nearly sufficient; only the scope of the returned list is 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?

The tool takes zero parameters, so the baseline of 4 applies; there are no argument semantics to explain. The description instead spends its words on what comes back, which is the right allocation here.

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 answers 'what is this' directly: it reports what data is loaded and enumerates the contents (row counts, date range, services/components, as-of date). This is a specific inventory/discovery resource rather than a metric query, which implicitly separates it from the metric siblings, though no sibling is named.

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, no prerequisite, and no reference to the metric siblings that make up the rest of the tool set. An agent can infer it is a discovery call, but the description never says to invoke it first to confirm coverage before running get_mttr, get_dora_metrics, etc.

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