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

With no annotations, the description carries the full disclosure burden. It adds real scope signal by naming the five audited domains (Sentry, OTel, metering, PII, environment), which implies a read-only audit, but it never confirms this is non-mutating, states permission/auth needs, or hints at whether findings are actionable.

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 no filler; the core purpose and the audited scope are front-loaded. Only a minor markdown list rather than prose would improve scannability.

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-parameter, no-output-schema tool, the description adequately conveys what is audited but says nothing about the shape or severity of results, which matters when no output schema exists to define them. It is minimally viable but leaves a real gap around the return values.

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 should ideally clarify the 'environment' parameter. It mentions 'environment settings', but this ambiguously refers to the audited config rather than the environment selector parameter, so it does not fully compensate for the coverage gap even though the enum and default are self-explanatory.

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 ('Audit') and resource ('observability configuration') and enumerates the audited areas (Sentry, OTel, metering, PII, environment), which is well beyond a tautology. It is clearly distinct from action-oriented siblings like sentry_resolve_issue or openmeter_dead_letter_retry, though it does not explicitly name an alternative to distinguish itself.

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 'for production readiness' implies a usage context, and listing the covered subsystems hints at when the tool is relevant. However, there is no explicit when-to-use/when-not guidance and no routing to related siblings such as check_conventions or diagnose_issue, leaving 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?

With no annotations, the description carries the full behavioral burden. It implies a read-only check but never states whether it is safe/non-mutating, what permissions are required, whether results are pass/fail versus a violation list, or any rate limits. Only the surface scope of the check is disclosed.

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 the verb and scope leading, and no filler. It is efficient, though arguably too terse for the behavioral context it omits.

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 validation tool with no output schema and no annotations, the description should explain the nature of the result (pass/fail, violation list) and any auth needs. None of that is present, leaving the agent under-informed about 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?

Schema description coverage is 100% for the single 'service' parameter, so the schema already documents it. The description adds no format or identification detail beyond what the schema provides, making the baseline 3 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?

States a specific verb (Verify) and resource (a service) and names the concrete checks performed: canonical names, required spans, attribute keys. This distinguishes it reasonably well from siblings like check_config_health, though it never explicitly names that sibling.

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 routing to alternatives such as check_config_health or diagnose_issue. The agent must infer the appropriate context on its own.

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 behavioral burden. It does disclose the scope ('across all projects'), which is useful, but it says nothing about return format, result limits, or what happens when neither/both identifiers are supplied — notable since both parameters are optional. That is a real gap 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?

Two short sentences, front-loaded with the action and identifier mechanism, with no filler or repetition. Every clause contributes 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?

With no annotations and no output schema, the description should cover more ground. It explains what the tool does but omits how the optional identifiers interact (is one required? what if both are given?) and gives no sense of the result shape, leaving the agent to guess on call construction.

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% (runId as UUID, traceId as 32-char hex), so the schema already documents both parameters. The description adds only the either/or relationship ('trace_id or run_id'), which is marginally useful but does not compensate for the unstated behavior when neither is provided. 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 ('Correlate errors across services') plus the mechanism ('using trace_id or run_id') and the outcome ('cross-service error timeline'). It is clearly distinct from generic listing tools, but it never names or contrasts with the obvious sibling sentry_search_by_trace, so the agent must infer the split.

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 'to build a cross-service error timeline' implies the scenario where this tool is appropriate, but there is no explicit when-to-use statement, no exclusions, and no pointer to sentry_search_by_trace as the single-trace alternative. Usage is inferable rather than stated.

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 provided, so the description carries the full behavioral burden. It doesn't disclose whether the tool invokes an LLM, whether it's read-only or has side effects, whether it's slow/expensive, or what form the diagnosis output takes. 'Deep root-cause analysis' is a meaningful hint but doesn't reveal operational traits.

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 efficient sentences, front-loaded with the core action and then the analytical dimensions. No filler; slightly could be improved by adding when-to-use guidance without bloating.

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-parameter, read-like analysis tool with no output schema and no annotations, the description gives a decent conceptual overview but omits important context: expected output format, cost/latency implications, and when this supersedes simpler sibling tools. It's adequate but leaves gaps an agent would need to guess.

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 there is only one parameter (issueId), which the schema already documents. The description adds nothing beyond what the schema provides, 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?

States a specific verb and resource ('root-cause analysis of a Sentry issue') and enumerates the analytical scope (stack trace, error patterns, frequency, platform knowledge). This clearly distinguishes it from siblings like sentry_get_issue (retrieval) and sentry_get_issue_events (raw events), which merely fetch data rather than analyze it.

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 a diagnostic use case, but offers no explicit when-to-use guidance nor names the sibling alternatives (e.g., 'use sentry_get_issue for raw data, diagnose_issue for analysis'). The 'deep' qualifier hints at escalation, but this is left 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?

No annotations are provided, so the description carries the full burden. It usefully discloses the structure of the generated artifact, which substitutes for a missing output schema, but says nothing about whether the result is persisted, whether generation is slow/LLM-backed, idempotency, or permissions required.

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 action and resource, then lists the output sections in a natural order. No filler or redundancy.

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 no-annotation, no-output-schema generator tool, the description covers the essential question of what the artifact contains, which is the main thing an agent needs. It falls short only on output format and the role of the optional service 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?

Schema description coverage is 100%, so the baseline is 3. The description reinforces the 'category' parameter ('for a specific error category') but does not explain how the optional 'service' parameter changes the generated runbook or what values are expected for either.

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 (Generate) and resource (incident runbook) scoped to a specific error category, and enumerates the sections the artifact will contain (triage, diagnosis, mitigation, resolution, postmortem). An agent can tell this apart from diagnose_issue or suggest_alert_rules, though no sibling is named 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?

There is no when-to-use, when-not-to-use, or alternative guidance. The phrasing 'for a specific error category' hints at the input context but never says when an agent should reach for a runbook versus running diagnose_issue or cross_correlate on the same incident.

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. It usefully discloses that the retry resets the attempt count so the outbox worker picks the events up again, but it omits permissions, rate limits, idempotency, and whether this can re-trigger failures.

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 no filler, and the core action plus its consequence are front-loaded. Every sentence earns its place.

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 mutation tool with no output schema and no annotations, the description covers the purpose and side effect but leaves parameter semantics and operational safety details incomplete. It is minimally adequate but not fully self-contained.

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 50%: orgId is documented in the schema, but maxEvents only has default and range constraints without a description. The tool description does not mention either parameter or explain that maxEvents caps how many dead-letter events are retried, 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?

The description uses a specific verb (Retry) and resource (dead-letter events that have failed max attempts), making the purpose immediately clear. It does not, however, differentiate from sibling tools such as openmeter_outbox_health, so it falls 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 qualifying condition — events that have failed max attempts — clearly indicates when this tool applies. There is no explicit when-not guidance or named alternative, but the context is sufficiently clear for the agent to route correctly.

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 behavioral burden. It discloses the metrics examined (pending events, dead letters, stuck leases, throughput), which helps the agent understand the output scope. However, it does not explicitly state that the operation is read-only, mention required permissions, or describe any side effects or rate limits.

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 wasted words. The colon-separated list of health aspects is efficient and scannable.

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 diagnostic read tool with one fully documented parameter and no output schema, the description is nearly complete: it states the purpose and the dimensions checked. It could be improved by explicitly noting that it is a read-only operation or by mentioning any expected return format, but the core information is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single 'hours' parameter is fully documented in the schema. The description adds no further meaning about the lookback window, 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 verb ('Check') and resource ('OpenMeter outbox queue health'), and enumerates the health aspects it examines. It implicitly distinguishes itself from sibling tools like openmeter_dead_letter_retry, but never names 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?

No when-to-use guidance, prerequisites, or comparison to alternatives is provided. The purpose implies monitoring, but the description does not tell the agent when this tool is preferable to openmeter_dead_letter_retry or other health diagnostics.

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. It does disclose the detection method (comparison to a rolling average, flagging spikes and drops), which is genuine context, but says nothing about return format, permissions, or the read-only nature of the 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?

A single tight sentence with the core detection concept front-loaded and no filler. It is appropriately sized, though it could have used a second short sentence for usage routing at no real cost.

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, and all three parameters are optional, so the description is the only source of behavior. It explains the detection logic adequately but omits what the tool returns (list of anomalies? per-org breakdown?) and any default-behavior guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters and their bounds. The description's mention of 'rolling average' loosely relates to baselineHours and spikeThreshold but adds no syntax or format detail beyond the schema. 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 (Detect) and resource (usage anomalies), and elaborates that the anomalies are sudden spikes or drops in token usage per org measured against a rolling average. This distinguishes it from the sibling openmeter_usage_by_org, which reports raw usage, though it never names that sibling 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?

No statement of when to use this tool, when not to, or which sibling to prefer. The purpose implies a monitoring use case, but an agent gets no explicit routing guidance among the many siblings (e.g. openmeter_usage_by_org, suggest_alert_rules).

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. It does not state that this is a read-only query, whether any scoping/permission constraints apply, or how the time window affects results. Only the output dimensions are implied.

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 fragment with no filler; the metric list is efficiently packed. It is appropriately sized for a simple two-parameter 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 read-only aggregation tool with no annotations and no output schema, the description names the returned metrics but omits the time-window behavior (hours, capped at 720) and any usage context. It is minimally adequate but leaves real 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 50%: orgId is documented in the schema, while hours is not described in the description at all. The schema itself supplies default (24), min (1) and max (720) for hours, which conveys most of its meaning, so the description neither compensates nor misleads.

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 (per-org usage breakdown) and enumerates the metrics returned (tokens, tool calls, costs by provider and model), so the agent knows what the tool produces. It does not explicitly distinguish itself from the sibling openmeter_usage_anomaly, so it falls 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 alternatives such as openmeter_usage_anomaly or openmeter_outbox_health. The agent must infer purpose 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?

With no annotations, the description carries the full burden. It usefully discloses the return payload (latest event stack trace, tags, contexts, cross-links), which matters because there is no output schema, but it says nothing about read-only safety, permissions, rate limits, or behavior on an invalid/unknown issue ID.

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 verb and resource, with every clause (stack trace, tags, contexts, cross-links) adding concrete information. 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?

For a single-required-param read tool with no output schema and no annotations, the description covers what the agent gets back and implies a safe read. Minor gaps remain around error behavior and ID sourcing, but the essentials are present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single issueId parameter, so the schema already documents it. The description adds no format, prefix, or sourcing guidance for the ID, 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 ("Get") and resource ("full Sentry issue detail") and enumerates the payload contents (stack trace, tags, contexts, cross-links). This distinguishes it from list-oriented siblings like sentry_list_issues and event-oriented sentry_get_issue_events, 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?

There is no when-to-use or when-not-to-use guidance and no reference to alternatives such as sentry_get_issue_events or diagnose_issue. Usage is only inferable from the fact that it takes an issue ID.

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, so the description carries the full burden. It mentions temporal pattern detection but omits how 'recent' is bounded, ordering, pagination, or return format. The 'detects temporal patterns' claim is unexplained, leaving the behavior opaque.

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 short sentences, front-loaded with the core purpose. The second sentence about pattern detection is arguably the most valuable but is terse and under-explained, wasting an opportunity.

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, no output schema, and an unexplained analysis feature, the description is too thin for an agent to call it confidently. Key details like time window, ordering, and what patterns mean are 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 50%: issueId is documented in the schema, but limit (with its default 25 and max 100) is undocumented both in schema and description. The description adds no parameter meaning beyond the schema, so baseline 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?

States a verb+resource ('Get recent events for a Sentry issue') which is somewhat clear, but 'recent' is vague and the second sentence about detecting temporal patterns muddies whether this is a fetch or an analysis tool. It does not differentiate from siblings like sentry_get_issue or diagnose_issue.

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 guidance. It doesn't explain when to reach for this over sentry_get_issue, diagnose_issue, or sentry_search_by_trace, which are natural alternatives in the 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?

No annotations are provided, so the description carries the full behavioral burden. It implies a read ('List') but never states read-only safety, and it omits important behavior such as the default 'is:unresolved' filter, pagination/limit behavior, or result ordering, which an agent needs to interpret the output correctly.

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 the core purpose front-loaded and no filler. It is efficient, though the second sentence is doing very little work beyond echoing the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 4-parameter listing tool with no annotations and no output schema, the description is thin. It omits the default query behavior, sort default, limit ceiling, and any notion of result shape or pagination, leaving the agent to reverse-engineer behavior from the schema alone.

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%: query and project are documented in the schema, while sort and limit are not documented anywhere. The description's 'Supports Sentry search syntax' merely restates the schema's own query description and adds no format, syntax, or constraint detail for the undocumented 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?

The description states a specific verb and resource ('List recent Sentry issues') plus scope ('for a project'), so an agent knows exactly what it returns. It stops short of naming how it differs from siblings like sentry_get_issue or sentry_search_by_trace, so the differentiation is only inferable from the tool names.

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 guidance and no alternatives are named. The only hint is that it supports Sentry search syntax, which tells the agent a query is possible but not when this tool should be chosen over sentry_get_issue, sentry_get_issue_events, or sentry_search_by_trace.

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 discloses nothing about authentication, rate limits, cost, or return shape. It doesn't even state that this is a read-only operation, nor that the 'stat' selection changes what is 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?

A single front-loaded sentence with no filler or redundancy. It is efficient, though the extreme brevity is part of why other dimensions are thin.

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 4-parameter tool with no annotations and no output schema, the description gives only the general return concept ('error rate trends'). It omits any explanation of what the enum parameters select and does not correct the fact that 'rejected'/'blacklisted' stats are not really error-rate trends.

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 only 50%: 'project' and 'period' are documented in the schema, but 'stat' (received/rejected/blacklisted) and 'interval' (1h/1d) have enums with no explanation. The description's phrase 'over a time range' loosely gestures at 'period' but adds no meaning for the undocumented enum 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?

The description states a specific verb and resource ('Get error rate trends for a Sentry project') and adds a scoping dimension ('over a time range'). It is clearly distinguishable from sibling readers like sentry_list_issues or sentry_get_issue, but it never names those siblings or otherwise differentiates itself 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?

There is no guidance on when to use this tool versus alternatives such as sentry_search_by_trace, sentry_get_issue_events, or the OpenMeter usage tools. No prerequisites, exclusions, or routing conditions are given; usage is only implied by the name and description.

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. It implies a mutation but says nothing about required permissions, whether the change is reversible, what side effects (muting alerts, notifications) occur, or what the response looks like. For a state-changing tool with zero annotation coverage this is a significant 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?

A single front-loaded sentence with the verb, resource, and enumerable actions, and no wasted words. It is appropriately terse, though the phrase 'Update a Sentry issue status' is mildly redundant with the tool name.

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 mutation tool with no annotations and no output schema, the description should at least mention return/confirmation behavior or the ignoreDuration/action coupling. The enum and param docs cover some ground, but the definition leaves important operational context 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 67%, with issueId and ignoreDuration documented in the schema and action explained by its enum. The description adds no syntax, format, or interaction details (e.g. ignoreDuration only applies when action is 'ignore'), so it sits at the baseline where 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?

States a specific verb (Update) plus resource (Sentry issue status) and enumerates the three supported actions, so an agent can tell this is the mutation tool rather than the read-oriented siblings like sentry_get_issue or sentry_list_issues. The name says 'resolve' but the description usefully reveals it also handles ignore/unresolve, adding value beyond the title. It does not name a sibling it must not be confused with, 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 lists what the actions are but gives no guidance on when to use each one, no prerequisites, and no mention of alternative tools. There is no when-to-use or when-not-to-use context, leaving selection entirely 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?

No annotations are provided, so the description carries the full behavioral burden. 'Find' implies a read-only lookup and 'across projects' discloses that the search spans projects by default, which is useful scope context, but nothing is said about pagination, result caps, rate limits, or auth requirements.

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 zero filler, front-loading the verb and resource before the scoping clause. Nothing could be removed without losing 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?

With no output schema and no annotations, the description should convey more about what comes back — 'Find all Sentry errors' hints at a list of error records, but result shape, ordering, and any limits remain unstated. Adequate but with clear gaps for a search 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 both parameters are documented there, so the baseline is 3. The description only marginally adds meaning, implying that 'project' is an optional narrowing filter via 'across projects' and that traceId is the primary key.

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 (Find), resource (Sentry errors), and the linking mechanism (OTel trace ID), with the scope 'across projects'. It is distinguishable from issue-list siblings like sentry_list_issues because the entry key is a trace ID, though it does not name any sibling 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?

There is no when-to-use guidance, no stated preconditions, and no mention of alternatives such as sentry_list_issues or cross_correlate. The agent must infer that this is the right tool when it already holds a trace ID.

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 doesn't clarify whether the tool only returns suggested rules or actually applies/writes configuration to Sentry, nor does it mention permissions, side effects, or rate behavior — a critical ambiguity for a tool whose name says 'suggest' but whose description says 'Generate'.

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 efficient sentence with no filler, front-loaded on the verb and resource. It is arguably too terse given the gaps, but there is no structural waste.

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, no output schema, and one of two parameters undocumented, the description leaves the agent guessing about output shape, mutation semantics, and the metric-alert parameter. For a tool this under-specified structurally, the description should do more.

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%: 'project' is documented in the schema, but 'includeMetricAlerts' (boolean, default true) is undocumented in both schema and description. The description adds no compensating detail about what error patterns are inspected or what toggling metric alerts changes.

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 ('Generate'), a concrete resource ('Sentry alert rule configurations'), and the basis for generation ('based on error patterns') scoped to a project. It is distinguishable from siblings like diagnose_issue or generate_runbook, though it doesn't explicitly contrast itself with any of 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?

No guidance on when to use this versus alternatives such as diagnose_issue or generate_runbook, and no prerequisites (e.g., that a project must already have error data). The agent must infer the trigger condition entirely.

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