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 discloses that write mode is required, but does not describe the effect of acknowledgement, whether the operation is idempotent, permission requirements beyond write mode, or what happens to the alert state.

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 short and front-loads the main action and write-mode requirement. The Args section is efficient and there is no unnecessary text, though the docstring-style formatting is slightly less polished than a tightly written prose definition.

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?

An output schema exists, so return values need not be explained. For a mutation tool with no annotations, however, the description is only minimally complete: it omits workflow context, state-change semantics, and alternative operations, which matter for correct tool selection.

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 does explain both parameters briefly: alert_id as the fired alert ID and comment as an operational acknowledgement comment. However, it does not clarify that comment is optional or provide format expectations, so the compensation is only partial.

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

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

The description states a specific action and resource: acknowledging or updating the status of an alert finding. It is clear what the tool does, though it does not explicitly distinguish itself from sibling tools such as splunk_manage_alert_suppression or splunk_get_alert_history.

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 notes that write mode is required, which is a useful prerequisite. However, it does not explain when to use this tool versus alternatives like alert suppression, alert history retrieval, or listing alerts, leaving usage largely implied by the tool name.

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 disclosure burden. 'Query' implies a read-only operation and the description notes that execution duration and audit logs are returned plus a failure-only filter, but it does not cover permissions, retention window, rate limits, or what 'recent' means.

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 short, front-loaded with the core purpose, and follows with a compact parameter list. Every sentence and argument description serves a clear function without 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?

The tool has an output schema, so the description need not document return values, and it explains all three parameters adequately. The main gap is the lack of annotation coverage and the vague 'recent' time scope, but for a simple audit-query tool the description is largely complete.

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%, but the description compensates by documenting all three parameters in prose: limit as the number of audit records with a default of 50, tool_filter as an optional tool-name filter, and only_failures as a failure-only switch. This adds clear meaning beyond the bare schema types, though it does not explain accepted values or bounds for tool_filter or limit.

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 ('Query') and clearly identifies the resource as recent MCP server tool invocations, execution duration, and audit logs. It does not explicitly differentiate itself from siblings such as splunk_search_history or splunk_get_search_performance, but the MCP-tool-activity scope is distinctive enough for an agent to understand the purpose.

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 usage through its opening scope and the only_failures filter, which supports failure-analysis scenarios. However, it gives no explicit when-to-use guidance, no exclusions, and no comparison to alternative tools, leaving usage largely inferred.

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, yet it discloses only that this is a 'composite tool' — a useful hint that multiple searches are dispatched and cost is therefore higher. It says nothing about read-only vs. mutating behavior, latency/cost, which data sources are correlated, or how 'high-fidelity' is achieved. Output schema existing removes the need to explain return values, but the operational profile is 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?

The opening sentence is front-loaded and efficient, but the indented Args block is largely a restatement of parameter names ('max_events: Maximum chronological timeline events', 'earliest_time: Incident timeline start'). Length is acceptable, yet several lines earn nothing beyond what the schema title already says.

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?

An output schema exists and the safety profile is undeclared, so the remaining burden is modest, but a composite/orchestrating tool with five parameters and zero annotation coverage should state at minimum what it aggregates and that it is a read-only, higher-cost call. The description leaves the agent guessing how it differs from single-entity investigation tools.

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 does enumerate all five parameters. Crucially it supplies the entity_type enum ('ip', 'user', 'host') and value examples that the schema lacks entirely, which is real added meaning. However, earliest_time/latest_time are described only as "Incident timeline start/end" without Splunk relative-time syntax, and max_events merely restates its own name.

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?

"Construct a high-fidelity chronological timeline for an incident entity" gives a specific verb and resource, and the "Composite tool" prefix signals it orchestrates multiple underlying queries rather than a single search. It does not name which sibling it replaces (e.g., splunk_investigate_ip/host/user or splunk_correlate_events), so the boundary is only weakly drawn.

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 statement, no exclusion, and no named alternative among heavily overlapping siblings like splunk_investigate_ip, splunk_correlate_events, or splunk_search_events. The agent must infer that a 'composite' timeline is the post-investigation aggregation step rather than the first call.

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 discloses the flagging semantics (disconnected = no data within threshold) but says nothing about permissions, whether the check is read-only, scope (all forwarders? all indexes?), or whether it triggers notifications. Reasonable but thin for a no-annotation diagnostic 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?

One sentence of purpose followed by parameter detail, with the core detection behavior front-loaded. The docstring-style 'Args:' block is slightly raw but adds real information rather than padding.

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?

An output schema exists, so return values need not be described. However, with no annotations and no mention of scope or what qualifies as a 'missing' forwarder (never-registered vs. gone-silent), the definition leaves gaps an agent would want filled before relying on it.

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%, yet the description explains the single parameter meaningfully: minutes without data before a forwarder is flagged, with the default restated. That is exactly the semantic the schema's bare 'integer' title lacks.

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: detects forwarders that stopped transmitting past a threshold. It is distinguishable in spirit from splunk_list_forwarders and splunk_get_forwarder_status, but the description never names or contrasts those siblings, so differentiation relies on 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?

Use for connectivity monitoring is implied by 'stopped transmitting data beyond expected threshold,' but there is no explicit when-to-use, when-not-to-use, or pointer to alternatives such as splunk_get_data_freshness or splunk_get_ingestion_lag, which overlap in intent.

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 implies a read-only diagnostic check, but it doesn't state the required permissions, whether it runs a background search that could be expensive, how 'active' is determined, or what happens to expected sources that are missing.

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 purpose sentence comes first, and the Args block is terse, with no filler. The Args block mostly duplicates schema titles, but it is not verbose.

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?

An output schema covers return values, but with zero schema coverage and zero annotations, the description should explain matching behavior: exact versus wildcard source matching, case sensitivity, and the semantics of the lookback window. It omits all of these for a three-parameter verification 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, and it only restates parameter titles. It usefully clarifies that expected_sources accepts source or sourcetype strings, which is real added meaning, but 'lookback: Time interval to inspect' never explains the '-4h' relative-time syntax or the accepted formats.

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: 'Verify that expected data sources are active and currently indexing data.' That clearly separates it from listing tools like splunk_list_sources or splunk_list_sourcetypes, but it never names a sibling or says what makes it different from splunk_get_data_freshness or splunk_search_data_gaps.

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 says what the tool does but never says when to call it instead of alternatives, or which prerequisites must be met first. There is no exclusion or routing guidance at all, even though several sibling tools cover overlapping monitoring intent.

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 but discloses little: nothing about permissions/index access needed, whether the two periods must be non-overlapping, whether this is read-only, or how large the result may be. The one useful behavioral hint is that metric_spl must end in a stats count or metric clause, but that is a parameter constraint rather than tool behavior.

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 purpose sentence followed by an args block — no filler. The docstring-style listing is slightly terse and the one-line param glosses could be a bit richer without bloat.

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?

An output schema exists, so return values needn't be explained, and the purpose plus param list cover the bare minimum. Missing pieces are the time-format conventions, overlap/default behavior, and any hint about the comparison result shape, which an agent must guess for a two-period diff 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 0% (all properties are title-only), so the description must compensate, and it does partially by naming each of the five params and their role. However, it never explains the timestamp format Splunk expects (relative strings like '-24h@h' vs ISO), nor the meaning of the defaults, nor what 'current' vs 'comparison' period means for pairing.

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?

First sentence gives a specific verb and resource — 'Compare event volume or metric values between two distinct time periods' — which is clearly distinct from sibling search tools like splunk_search or splunk_search_events. It falls short of 5 because it doesn't explicitly name the alternative it replaces (e.g., running two searches manually).

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 versus splunk_search, splunk_detect_anomalies, or splunk_search_data_gaps. The only guidance is the implicit requirement that metric_spl end in a stats count/metric, which constrains the query but doesn't tell an agent when the tool is the right choice.

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 disclosure burden. It explains the time-window behavior and default timestamp but does not state whether the operation is read-only, what permissions are required, whether it is expensive, which data sources are correlated, or any limits on the correlation search.

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 compact and front-loaded: the purpose appears first, followed by a concise Args section. Every sentence and parameter note contributes useful information with no wasted text.

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?

An output schema exists, so the description need not explain return values. However, with no annotations and a broad correlation purpose in a dense sibling set, the description is only minimally complete: it explains the parameters but omits operational context such as data sources, performance expectations, permissions, and when to prefer this tool over nearby investigation or timeline tools.

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 documents all three parameters meaningfully: entity_value as an IP/username/hostname identifier, window_minutes as the before-and-after window, and timestamp as an epoch or ISO anchor with current time as default. Minor gaps remain, such as not restating the window_minutes default of 30 or describing boundary inclusivity.

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: correlate multi-source events within a synchronized time window around an entity. It is clear what the tool does, though it does not explicitly differentiate itself from related siblings such as splunk_investigate_ip, splunk_investigate_user, or splunk_build_incident_timeline.

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 provides no explicit guidance on when to use this tool versus alternatives, nor does it state exclusions or prerequisites. Usage is only implied by the purpose statement and parameter 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?

With no annotations, the description carries the full burden. It discloses the write-mode requirement and that the payload may be either Simple XML or Dashboard Studio JSON, which is genuinely useful. It does not say whether a duplicate 'name' overwrites or errors, nor anything about permissions beyond write mode, so key mutation risk remains undisclosed.

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 purpose sentence followed by a compact Args block; no filler. The small cost is that the Args block is the only structural element and the purpose line does not preview the prerequisite prominently.

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?

An output schema exists, so return values need not be explained. But for a mutation tool with zero annotations, the definition omits collision/overwrite behavior and error conditions an agent should anticipate 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 description coverage is 0%, so the description must compensate, and it does: it clarifies 'name' is a unique view ID, 'label' is the display title, 'app' defaults to 'search', and critically that 'xml_data' accepts either XML or JSON despite its name. Minor remaining ambiguity (how label behaves on duplicate, whether xml_data is validated) keeps it from a 5.

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 ('Create a new Simple XML or Dashboard Studio dashboard'), which clearly separates it from sibling mutators like splunk_update_dashboard and splunk_delete_dashboard. It does not explicitly name those siblings, so it stops short of full differentiation.

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 parenthetical '(requires write mode)' gives a real prerequisite, which is useful gating information. However, there is no guidance on when to create vs. update an existing dashboard, or what to do if the name already exists.

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 discloses the write-mode requirement and states the aging-to-frozen behavior, which is meaningful context for a mutating tool, but it omits failure modes (duplicate name), irreversibility, and permission 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?

The core action and the write-mode constraint are front-loaded in one sentence, followed by a tight Args list. The Args entries slightly restate the schema's defaults, but each still 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?

With an output schema present, return values need not be explained, and all three parameters are documented. A creation tool with no annotations would ideally state duplicate-name behavior and permission requirements, which is the remaining 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%, so the Args block does the work: it explains that name must be unique, that max_size_mb is in megabytes with a 10,000 MB default, and that frozen_retention_days is the window before aging to frozen. All three parameters gain meaning beyond the bare schema types, though units for the retention field could be restated.

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 ('Create a new Splunk index') plus the dimensions it controls (storage capacity, retention limits), which distinguishes it from the read-side siblings like splunk_get_index and splunk_list_indexes. It stops short of explicitly naming splunk_update_index_settings as the alternative for existing indexes.

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?

'(requires write mode)' gives one real precondition for use. There is no guidance on when to prefer this over splunk_update_index_settings, no mention of admin/permission prerequisites, and no statement about what happens if the index name already exists.

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 disclosure burden, and it does contribute one important behavioral fact: write mode is required. Beyond that it says nothing about duplicate-name handling, whether the search executes on creation, or what errors to expect, which are material gaps for a mutation 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?

Front-loaded with the purpose sentence before the parameter list, so the agent gets the essential action immediately. The args block is appropriately sized for six undocumented parameters, though a few entries restate the parameter name without adding meaning.

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?

An output schema exists, so return values need not be described, and all six parameters get some coverage. For a write operation with zero annotations, though, the definition omits failure modes (duplicate name, invalid cron, nonexistent app namespace) that an agent would need before committing a mutation.

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 docstring arg list does real work: it explains that 'search' is an SPL query, gives a cron syntax example, and states the 'app' default of 'search'. Some entries are near-tautological ('description: Description of the report/saved search'), and the is_scheduled/cron_schedule interaction is not clarified.

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 ('Create a new saved search or scheduled report'), which is immediately distinguishable from siblings like splunk_update_saved_search and splunk_list_saved_searches by verb alone. It never names those alternatives explicitly, so 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 parenthetical '(requires write mode)' gives one concrete prerequisite for invoking the tool, which is genuinely useful. However, there is no guidance on when to create versus update or dispatch an existing saved search, and the relationship between is_scheduled and cron_schedule is left for the agent to infer.

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

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

No annotations are provided, so the description carries the full burden, and it does disclose two meaningful traits: write mode is required and human confirmation is expected. What it omits matters for a destructive tool — that the deletion is irreversible, what happens if the name does not exist, and whether the delete is scoped to the app namespace or global. Partial disclosure for a destructive operation lands at 3.

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 operative sentence comes first and is followed by a compact, scannable Args list with no filler or marketing language. Minor redundancy in restating parameter names that are already in the schema, but nothing is bloated. Front-loading the destructive nature before the parameters is the right structure.

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?

An output schema exists, so return values need not be explained, and the parameter list is fully enumerated. For a three-parameter destructive tool with no annotations, though, the description leaves out irreversibility, error behavior on a missing name, and app-scope semantics — gaps an agent would need filled before calling confidently.

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 partly does via an Args block describing all three parameters. But the semantics are thin: "confirmation flag" restates the parameter name without saying it must be true for the call to succeed, and "optional app namespace" does not explain the default scope when omitted. Helpful but not sufficient given zero schema coverage.

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 first sentence gives a specific verb and resource ("Delete a dashboard") that unambiguously separates it from siblings like splunk_create_dashboard, splunk_update_dashboard, and splunk_get_dashboard. It also front-loads the two preconditions in the same sentence rather than burying them. It stops short of a 5 because it never names the sibling tool a caller might confuse it with.

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?

"Requires write mode and human confirmation" implicitly tells the agent this is only appropriate for an authenticated, user-sanctioned removal — a real usage constraint. However, there is no explicit guidance on when to delete versus archive or rename via splunk_update_dashboard, and no statement about what to do if the target is the wrong dashboard. Usage is implied, not spelled out.

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 write-mode requirement and human confirmation, but it does not explain irreversibility, side effects, or how the confirm flag interacts with the confirmation requirement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded, direct, and free of filler. The key operation and its preconditions appear in the first sentence.

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 destructive tool with no annotations but an output schema, the description covers the essential operational context: deletion, write mode, and human confirmation. It could be stronger by clarifying confirm semantics, but it is largely complete.

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 compensate. It names both parameters ('name' and 'confirm') but gives only minimal semantics, especially for 'confirm: Confirmation flag', which does not clarify that setting it to true is what actually authorizes deletion.

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: 'Delete an existing saved search'. It is immediately distinguishable from sibling operations such as splunk_create_saved_search, splunk_update_saved_search, and splunk_get_saved_search.

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 clear preconditions for use: 'requires write mode and human confirmation'. It does not explicitly name alternatives or when not to use it, but the delete operation is unambiguous against its create/update/get siblings.

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 must carry the full behavioral burden. It discloses the statistical method (standard deviation outliers), which is genuinely useful, but says nothing about whether this runs a search job, cost/permission implications, read-only safety, or how many anomalies are returned. A mutation-free read tool still needs more than algorithm hints.

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 opening sentence is front-loaded with the core purpose and the Args list is compact, with no filler. Slightly weakened by the Args entries being near-verbatim parameter names.

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?

An output schema exists, so return values need not be described. However, for a detection tool with zero annotations and no usage guidance, the definition leaves the agent unsure when to call it and what operational side effects 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 0%, so the Args block does real work: it explains that index is the index to inspect, sourcetype is optional, field_to_analyze defaults to 'count', and time_span is the aggregation bucket. It stops short of specifying time_span syntax (e.g. '1h', '5m') or the semantics of analyzing 'count' as a field.

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: 'Execute statistical anomaly detection (outliers based on standard deviation) over time series,' and even names the technique. That is clear and actionable, but it never distinguishes itself from adjacent analytical siblings such as splunk_compare_time_periods or splunk_detect_data_quality_issues.

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 only content beyond the opening line is an Args block that restates the parameters. There is no statement of when to prefer this over splunk_search or splunk_compare_time_periods, no prerequisites, and no exclusions.

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 detection logic (thresholding repeated failures over a binned window), but never states that this is a read-only analysis, what the grouping dimension is (per user, per IP, per host), or how expensive the underlying search is.

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 purpose followed by a compact Args block with defaults; no filler and the objective is front-loaded. The Args list is functional but thin relative to the schema it mirrors.

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?

An output schema exists, so return values need not be described. Still, for a security detection tool with zero annotations, the definition omits the detection scope (which log sources/fields, group-by key) and whether the agent can expect distinct entities or raw bins.

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%, yet the description documents all three parameters with defaults and intent, which compensates well. It is slightly weaker on 'time_window: Bin duration', which doesn't clarify the accepted format or what the bin is grouped by.

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 detection objective ('Detect potential brute force attacks or credential stuffing based on repeated failed logins'), which is clear enough to act on. It does not, however, differentiate itself from the sibling splunk_detect_failed_logins, which sounds like an adjacent/overlapping tool.

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 guidance on when to use this versus splunk_detect_failed_logins, splunk_investigate_ip, or the general search tools. The only implicit cue is 'repeated failed logins,' which leaves the agent to infer the aggregation/threshold distinction 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 are provided, so the description carries the full behavioral burden. It discloses the audit targets (nulls, timestamp skew, format discrepancies), which is useful, but it does not state whether the operation is read-only, what permissions are required, how much data is scanned, or whether there are time-range defaults or 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 short and front-loaded: the audit purpose and checks come first, followed by concise parameter notes. There is little wasted text, though the Args block mostly repeats 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?

An output schema exists, so return values need not be explained. However, for an audit tool with no annotations and zero schema-description coverage, the description is only minimally complete: it lacks when-to-use guidance, read-only behavior, and any default time-range or scan-scope context.

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 does identify the two parameters and marks sourcetype as optional, which is minimally helpful, but the explanations ('Index to audit', 'Optional sourcetype filter') are largely tautological and add little beyond the parameter names.

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 (audit) and resource (index/sourcetype), and enumerates the exact checks performed: null values, timestamp skew, and format discrepancies. It is clear what the tool does, though it does not explicitly differentiate itself from nearby data-diagnosis siblings such as splunk_get_parsing_errors or splunk_check_source_coverage.

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 audit framing and optional sourcetype filter, but there is no explicit guidance on when to choose this tool over alternatives like splunk_diagnose_ingestion or splunk_get_field_summary. The agent must infer that this is for data-quality auditing rather than broader ingestion diagnosis.

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, yet it says nothing about permissions, cost/timeouts, or how 'aggregate' works (grouped by user, host, source?). It hints at cross-source scoping but leaves the key aggregation 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?

Purpose is front-loaded in the first sentence, followed by a compact Args breakdown. No wasted text; the structure is easy to scan.

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 full output schema exists so return values needn't be explained, and all three params are covered. However, the description omits the aggregation grouping semantics and when-to-use context, which matter for interpreting results of a detection tool.

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

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

With 0% schema description coverage, the description compensates well: it explains index defaults to _audit and default indexes (beyond the schema's null default), gives the earliest_time default, and defines count as max aggregated rows. This adds real meaning beyond the bare 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 ('Detect and aggregate') and resource ('failed authentication events') across named sources (Splunk audit, Windows, Linux logs). It is clearly distinguishable from generic search tools, but it does not explicitly contrast itself with the nearby splunk_detect_brute_force 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?

No guidance on when to use this versus alternatives such as splunk_detect_brute_force, splunk_investigate_user, or splunk_search. Prerequisites and selection criteria are left 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 burden. It doesn't say what data source is queried (job history vs. search endpoint), whether it requires admin permissions, or what 'expensive' is measured by beyond duration. Only the read-only nature is implied by 'Identify'.

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 summary sentence plus a short args list; front-loaded and free of padding. The Args block is functional, though terse enough that some entries read as labels rather than explanations.

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?

An output schema exists, so return values needn't be explained. But for a diagnostics tool with zero annotations and 0% schema coverage, the description should at least clarify the time-format convention and the notion of 'expensive' — those gaps remain.

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 does compensate by explaining all three parameters. However 'earliest_time: Search window' gives no format hint (the schema default '-24h@h' is unexplained) and 'count' is described only as 'Maximum slow searches to list', so the compensation is partial.

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?

Clear specific verb+resource: identifies slow/expensive searches above a duration threshold. This distinguishes it reasonably from siblings like splunk_get_search_performance or splunk_search_history, 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?

No statement of when to use this versus splunk_get_search_performance, splunk_get_search_concurrency, or splunk_search_history, all of which overlap in the 'search cost/perf' space. The agent must infer the choice from names 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?

No annotations are provided, so the description carries the behavioral burden. It discloses that this is a composite evaluation of several ingestion-health signals, which is useful, but it does not explicitly state that it is read-only, whether it runs heavy queries, or what permissions/timeouts apply.

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 text. It efficiently names the tool type and the specific health dimensions being evaluated.

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 parameters and an output schema present, the description is complete enough to invoke safely, and it indicates the scope of the diagnostic. However, in a large ecosystem of ingestion-specific siblings, it does not fully explain when this composite tool is preferable to the granular alternatives.

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 the baseline is 4. The description does not need to explain input semantics, and the empty schema is consistent with a no-argument diagnostic.

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 (evaluate) and resource (overall pipeline health) and lists concrete diagnostic aspects: queue sizes, drop rates, and bottlenecks. The word 'composite' signals it is an aggregate diagnostic rather than a granular metric fetch, though it does not explicitly name the sibling tools it replaces.

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, and no alternatives are named. 'Composite diagnostic' implies a broad overview, but the description never tells the agent to choose this over granular siblings such as splunk_get_ingestion_metrics, splunk_get_ingestion_lag, or splunk_get_parsing_errors.

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-disclosure burden. It lists assessed dimensions but says nothing about whether the tool is read-only, how expensive the multi-dimensional assessment is, what permissions are needed, or whether it triggers searches.

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. It immediately communicates the composite nature and the dimensions assessed, wasting no words.

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?

An output schema exists, so return values need not be explained. The description adequately names the dimensions, but it lacks guidance on when this composite tool is preferable to targeted sibling diagnostics and omits behavioral context such as cost or safety.

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 document. The baseline score of 4 applies because the schema is trivially complete and the description does not need to compensate for undocumented inputs.

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 clearly identifies the tool as a composite performance assessment covering CPU, memory, scheduler, disk, and search latency. This distinguishes it from single-domain siblings like splunk_get_scheduler_health and splunk_get_search_performance, though the phrasing is a noun phrase rather than a direct verb+resource statement.

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 only implies broad use by calling it a 'Composite tool' and listing dimensions. It does not state when to choose this over targeted siblings such as splunk_get_scheduler_health or splunk_get_server_health, nor does it provide exclusions or triage 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 burden of behavioral disclosure. It usefully states that the tool immediately runs the search, returns a newly generated job SID, and that trigger_actions can fire configured alert actions, but it omits permissions, job lifecycle/async behavior, and any side effects beyond alert actions.

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 first sentence front-loads the action and return value, and the short Args section documents the two parameters without unnecessary padding. Every sentence serves a clear purpose.

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

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

For an action-oriented tool with no annotations and low schema description coverage, the definition covers the parameters and return value adequately. However, it lacks guidance on alternatives, permissions, and job lifecycle behavior, which leaves meaningful gaps for an agent deciding whether and how to invoke it.

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 does explain both parameters: name identifies the saved search, and trigger_actions controls whether configured alert actions fire, with examples. The only minor gap is that it does not restate the default for trigger_actions, though the schema provides it.

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: dispatch/run a saved search, plus the return value (newly generated job SID). It is clear what the tool does, but it does not differentiate itself from closely related siblings such as splunk_replay_saved_search, splunk_search_async, or splunk_run_report.

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 guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The usage is only implied by the purpose statement, leaving the agent to infer when dispatching a saved search is preferable to replaying one or launching an async search.

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 supplied, so the description carries the full disclosure burden, and it says nothing about side effects, permissions required, whether workflows mutate state, expected runtime, or whether execution is synchronous. 'Execute' strongly implies a potentially consequential action, and for a tool with zero annotation coverage that is a material 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?

Two short sections, front-loaded with the action and followed by argument guidance; no filler sentences. The Args formatting is slightly redundant with the schema structure but the content itself is dense and useful.

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?

An output schema exists, so return values need not be explained, and the two inputs are addressed. What is missing is behavioral context around execution — side effects, duration, and per-workflow parameter contracts — which matters for a tool that runs multi-step pipelines.

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 reasonably well: it enumerates the three valid workflow_name values (information absent from the schema, which merely types it as a string) and gives a concrete example shape for the free-form parameters object. The parameters field remains open-ended, so an agent still cannot know which keys a given workflow expects.

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 (Execute) and resource (automated investigation or operational workflow pipeline), and the Args section enumerates the concrete workflows available, which makes the tool's domain tangible. It does not, however, distinguish this meta-workflow from narrower siblings like splunk_investigate_ip or splunk_diagnose_ingestion that cover overlapping investigative territory.

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?

Naming 'incident_triage', 'ingestion_audit' and 'capacity_review' implicitly tells an agent what situations fit, but there is no explicit when-to-use/when-not, no prerequisites, and no statement about how this differs from chaining the individual detector tools that already exist as siblings.

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 discloses the analytical transformations (interpretation, distribution highlighting, summarization), but does not state whether the operation is read-only, whether it re-runs or accesses Splunk, or any privacy/rate-limit considerations. The output schema covers return structure, but key behavioral context is 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?

The description is front-loaded, compact, and free of filler. The Args section maps cleanly to the two parameters without repeating schema boilerplate.

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 two parameters, an output schema, and no annotations, the description covers the core purpose and both inputs adequately. However, it is not complete enough to help an agent confidently select this tool over the many related analysis siblings, and it omits behavioral details such as read-only nature or whether it performs any Splunk-side operations.

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 does explain both parameters: `results` is an array of result dictionaries from a search, and `question` is an optional analytical question. This is meaningful beyond the bare schema titles, though it does not specify result array size or shape constraints.

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 set and resource: interpret search results, highlight statistical distributions, and summarize findings. It is clear that this is a post-processing/analysis tool over an existing results array. However, it does not differentiate itself from nearby analysis siblings such as splunk_search_explain, splunk_get_field_summary, or splunk_detect_anomalies.

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 required `results` parameter: call this after obtaining search results to interpret them. The optional `question` parameter hints at answering a specific analytical question. There is no explicit when-to-use, when-not-to-use, or named alternative among the many sibling tools.

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 mentions output formats and a maximum event count but does not disclose whether the tool executes a new search, whether it is read-only, what permissions are required, or any execution limits or side effects.

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 short and front-loaded with the core purpose, followed by a compact argument list. It is efficient, though the Args list repeats parameter names already present in 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?

An output schema exists, so return values need not be explained. However, for a tool with no annotations and four parameters, the description should clarify execution semantics, permissions, and limits; it covers the parameter surface but leaves important behavioral context unspecified.

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 for all four parameters. It documents each parameter name and adds some meaning, such as the allowed output_format values and the purpose of count and earliest_time, but it omits the default count value (500), the expected format of earliest_time, and any constraints.

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: exporting search results in JSON, CSV, or raw format. This clearly distinguishes it as an export tool, but it does not explicitly differentiate it from close siblings such as splunk_search_results or splunk_search_events.

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 provides no explicit guidance on when to use this tool versus alternatives like splunk_search_results or splunk_search_events. Usage is only implied by the word 'Export', leaving the agent to infer the appropriate context.

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 disclosure burden. It does add useful behavior: the output is a Markdown-formatted executive report, not raw data. However, it says nothing about cost or latency (this presumably runs multiple internal searches), whether the timeframe is fixed or configurable, or any permission 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 well-formed sentence that front-loads the verb and outcome, names the output format, and enumerates coverage. Zero wasted words.

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?

An output schema exists, so return values need not be explained. But for a report-generation tool with no parameters, the description omits the reporting window (what time range the license/error/storage figures cover) and whether the report is expensive to produce — meaningful gaps for an agent deciding whether to call it.

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 and the schema is fully described, so there is nothing for the description to disambiguate. Baseline for a no-parameter tool 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 (Generate) and a concrete artifact (executive Markdown operational health report) plus the three domains it covers: license, errors, storage. This distinguishes it from the individual sibling tools like splunk_get_license_usage and splunk_get_internal_errors, which report single domains rather than an aggregated health report.

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 word 'executive' and the multi-domain scope imply it should be chosen when a consolidated summary is wanted rather than individual lookups, but the description never states when to use this versus calling splunk_get_license_usage / splunk_get_internal_errors / splunk_get_storage_usage separately. Usage must be inferred.

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 implies a read operation but says nothing about permissions required, pagination or result caps, whether the window implicitly ends 'now' (there is no latest_time), or ordering of returned events. The only behavioral hint is the default window '-7d@d'.

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 lines of purpose followed by a terse Args block; every sentence pays for itself and the core purpose is front-loaded. Nothing is padded or restated unnecessarily.

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?

An output schema exists, so return shape needn't be explained, and all three parameters are at least named. But with no annotations and no required parameters, the description should still cover the time-window boundary semantics, result limits, and how this differs from listing configured alerts.

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 does list all three parameters with brief meaning plus defaults. However the semantics are thin: 'count: Maximum history events' doesn't say whether it's total events or per-alert, and no time-syntax or range guidance is given beyond the '-7d@d' example.

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 opens with a specific verb and resource: 'Retrieve historical fired alert events over a time window.' That is clearly distinct from configuration-oriented siblings like splunk_list_alerts or splunk_get_saved_search, though it never names those siblings explicitly. An agent can tell what it returns, just not the boundary against related alert tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use statement and no alternatives offered. In a sibling set containing splunk_list_alerts, splunk_get_security_alerts, splunk_acknowledge_alert and splunk_manage_alert_suppression, the agent is left to infer on its own that this tool is for past firing events rather than alert configuration or triage workflows.

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 the three information categories returned (metadata, configurations, permissions), which signals a read operation, but says nothing about permission requirements, behavior for a nonexistent app name, or whether the call can be expensive.

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 of purpose followed by a compact Args block; front-loaded and free of filler. The pseudo-Python arg formatting is slightly noisy but not wasteful.

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?

An output schema exists, so return values needn't be explained, and the lone parameter is covered. What remains missing is how to obtain a valid app name and how the tool behaves on an unknown name, which matters for a single-arg lookup 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 0%, so the description must define the single parameter — and it does, clarifying that 'name' is the app folder name rather than a display name, with concrete examples ('search', 'SplunkForwarder'). It could have added a pointer to splunk_list_apps for discovering valid names.

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 ('Get metadata, configurations, and permissions for a specific Splunk app'), which is clearly distinct from splunk_list_apps and splunk_manage_app. It doesn't explicitly name those siblings, so sibling 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?

No indication of when to use this instead of splunk_list_apps (to discover app names) or splunk_manage_app (to change app settings). The agent must infer the read-vs-list-vs-mutate routing entirely 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 are provided, so the description carries the full behavioral burden. 'Check health status' implies a read-only operation, but there is no explicit statement about safety, side effects, permissions, or behavior for different topologies beyond a brief parenthetical. This leaves meaningful gaps for a tool with zero 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, front-loaded sentence with no wasted words. The scope and the fallback case (standalone topology) are stated efficiently.

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 tool takes no parameters and has an output schema, so the description needn't explain return values. It adequately covers the intended topologies (indexer cluster, search head cluster, standalone). Minor gap: it doesn't hint at what health aspects are reported, but the output schema fills that in.

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 zero parameters, the baseline is 4. The description requires no parameter explanation and the schema is trivially complete, so this dimension is well satisfied by default.

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 ('Check') and resource ('health status of indexer cluster or search head cluster') and also covers standalone topology. An agent can distinguish this from siblings like splunk_get_server_health or splunk_get_replication_status without opening schemas.

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 when to use the tool (cluster health checks or standalone topology reporting), but gives no explicit guidance about alternatives or exclusions. It does not say when to prefer this over splunk_get_replication_status or splunk_get_server_health, 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?

No annotations are provided, so the description carries the safety disclosure burden. It helpfully states that secrets are scrubbed and implies a read operation, but it does not mention permissions, scope when stanza is omitted, or other operational constraints.

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 short and front-loaded, with the core purpose stated first and a compact Args block. Every sentence adds relevant information without 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?

An output schema exists, so the description need not explain return values. It covers the two input parameters and discloses secret scrubbing, though it could better route the agent relative to sibling configuration tools.

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 names both parameters and gives useful conf_file examples, but stanza semantics are only minimally described as an optional specific stanza name, with no format or wildcard details.

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: read stanzas and properties from a configuration file, with secrets scrubbed. It is clear what the tool does, but it does not distinguish itself from related siblings such as splunk_list_configurations or splunk_validate_configuration.

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 does not say when to use this tool versus splunk_list_configurations, splunk_validate_configuration, or other configuration-related tools. It only documents the arguments, leaving usage conditions 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 and does it thinly. It implies a read-only fetch, but 'XML or JSON' suggests a selectable format with no corresponding parameter and no explanation of how the format is determined — a genuine ambiguity for the caller.

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 plus a short Args block, front-loaded with the core action. No filler, though the Args block largely duplicates what the schema already declares.

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?

An output schema exists so return values needn't be described, and the tool is a simple 2-param read. Still, the XML/JSON format question and the missing-namespace behavior are unresolved, and with zero annotations the description should be doing more of that work.

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 does partially by glossing 'name' as 'Dashboard view name/id' and 'app' as 'Optional app namespace'. However it does not explain what happens when 'app' is omitted (default namespace?) or what form the name/id takes, leaving real gaps.

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

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

Names a specific verb ('Retrieve') and resource ('dashboard') with scope ('full XML or JSON definition'), which distinguishes it from siblings like splunk_list_dashboards (summaries) and splunk_update_dashboard (writes). It stops short of naming an alternative explicitly, but the distinction is still inferable.

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 splunk_list_dashboards or splunk_get_app_details, and no prerequisites or follow-up steps mentioned. The agent must infer usage entirely from the tool name.

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 at all, so the description carries the full burden. It names the three metrics evaluated, which is mild useful context, but says nothing about permissions required, whether the index must exist, how time ranges are determined, or any latency/cost implications of the call.

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?

Short and front-loaded: the capability sentence comes first and the args block is minimal. Little waste, though the parameter lines add nearly zero information for the tokens spent.

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?

An output schema exists so return values needn't be explained, but for a diagnostic tool in a crowded ingestion-diagnostics family the description should at least distinguish its role from splunk_get_ingestion_lag and splunk_get_ingestion_metrics. With no annotations and 0% schema coverage, the remaining gaps are substantial.

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 does not. 'index: Target index' and 'sourcetype: Optional sourcetype' are tautological restatements of the names with no format hints (exact name vs. pattern, wildcard support, whether sourcetype affects the freshness window).

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 ('Evaluate') and resource (data recency, latest event timestamp, ingestion latency), so the agent knows what the tool returns. However it offers no differentiation from close siblings like splunk_get_ingestion_lag, splunk_get_ingestion_metrics, or splunk_check_source_coverage, which overlap heavily in purpose.

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 the many ingestion-related siblings (lag, metrics, source coverage, diagnose_ingestion). No prerequisites, no exclusions, no context about what scenario makes this the right choice.

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 that the summary is based on sampled events via sample_count and that it returns statistical distributions and top values, which is useful. However, it omits permissions, cost/rate-limit implications, and whether the operation is strictly read-only or executes a search.

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 purpose is front-loaded in the first sentence, and the Args block is compact. There is little waste, though the parameter list could be formatted more cleanly for machine 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?

The presence of an output schema means return values need not be explained, and the description covers the basic inputs. Still, with no annotations and 0% schema coverage, it lacks enough behavioral and usage context to be fully complete for an agent choosing between this and related search/sampling tools.

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 for all three parameters. It does briefly document every parameter (index, sourcetype filter, sample_count), but it omits important semantics such as default value, allowed ranges, and exact sourcetype matching behavior.

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: obtain statistical distribution of fields and top values for an index and sourcetype. It is clear what the tool does, but it does not explicitly distinguish itself from adjacent tools like splunk_sample_events or splunk_search, which could also surface field/value information.

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, no when-not-to-use condition, and no named alternative. The description implies it is for field distribution summaries, but an agent must infer when this is preferable to splunk_sample_events, splunk_search, or splunk_list_sourcetypes.

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 signal a read-only inspection by naming observable metrics (volume, drops, status) rather than actions. However, it says nothing about permissions/authentication needed, whether the data is real-time or historical, or how transmission volume is measured, which leaves meaningful behavioral gaps for an unannotated 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?

The operative sentence is front-loaded and free of padding. The 'Args:' block adds little since the single parameter is already named identically in the schema, so there is minor 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 one-parameter read tool with an output schema (return values needn't be explained), this is close to adequate, but it omits how to obtain a valid hostname (e.g., via splunk_list_forwarders) and whether results reflect current or historical forwarder state.

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 only one parameter and 0% schema description coverage, the description must compensate, and 'hostname: Forwarder hostname' merely restates the field name. It does not say whether an FQDN, short name, or splunkd server name is expected, nor whether case matters — the kind of detail that prevents a failed or empty call.

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 ('Inspect') and a concrete set of resources (transmission volume, connection drops, status) scoped to 'a specific forwarder', which is more informative than the name alone. It stops short of differentiating itself from close siblings such as splunk_list_forwarders and splunk_check_forwarder_connectivity, so an agent must infer the 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?

There is no explicit guidance on when to use this versus splunk_list_forwarders (all forwarders) or splunk_check_forwarder_connectivity (reachability). The phrase 'for a specific forwarder' hints at per-host usage, but no exclusion or condition is 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, so the description carries the burden; 'Query' does imply a read-only operation, which is the correct safety signal. But it doesn't say what happens when the index doesn't exist, whether elevated roles are required, or what access scope is needed — meaningful gaps for an unannotated tool. An output schema is present, so omitting return-value detail is acceptable.

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 compact lines with the capability front-loaded and the parameter after it. The 'Args:' block is slightly formal for a single parameter but costs almost nothing; 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 one-parameter read tool with an output schema and required 'name' documented with examples, an agent has what it needs to call it. The only real omission is what to do when the index is unknown and how it relates to list_indexes, which is a routing concern rather than a calling one.

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 does, giving both the meaning ('Index name') and concrete examples ('main', '_internal', 'wineventlog') that go beyond the bare {type: string, title: Name} property. That is enough to invoke correctly; only whitespace/case-sensitivity conventions are absent.

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 ('Query configuration, retention policies, and storage paths for a specific index'), naming exactly what data comes back, which is more informative than a bare 'get index'. It implicitly distinguishes itself from list-style siblings by scoping to 'a specific index', but never names splunk_list_indexes or splunk_index_stats, so the 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?

No when-to-use, when-not-to-use, or prerequisite guidance. An agent would have to deduce on its own that splunk_list_indexes is the way to discover a valid index name first, and there's no signal about when to prefer splunk_index_stats or splunk_get_configuration over 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 carries the full disclosure burden, and it discloses little. It hints at sampling via 'Sample size of events' but says nothing about query cost, time-range assumptions, required permissions, or whether the measurement is a single statistic or per-stage breakdown. The presence of an output schema offsets some of this, but the behavioral profile of a search-backed diagnostic remains 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?

The core definition is one tight, front-loaded sentence, followed by a compact Args block. The docstring formatting is slightly verbose relative to the content but nothing is wasted 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?

Return values need not be explained because an output schema exists, and all three parameters are glossed. What is missing is the operational context a diagnostic tool needs: what a 'high' lag means, whether it queries a fixed time window, and how it relates to the sibling ingestion tools.

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: 'index' is the index to evaluate, 'sourcetype' is an optional filter, and 'count' is the number of events sampled. That last gloss is the valuable one, since the bare 'Count' title could otherwise mean a result limit rather than a sample size. It omits the default of 100 and any bounds on count.

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 and measurable resource: it measures pipeline lag between event occurrence timestamp and indexing time. That is far more precise than the sibling 'splunk_get_data_freshness' or 'splunk_get_ingestion_metrics', but it never names those siblings to make the distinction unambiguous for an agent choosing between 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 statement of when to use this tool versus splunk_get_data_freshness, splunk_get_ingestion_metrics, or splunk_diagnose_ingestion, all of which live in the same troubleshooting territory. Only the parameter list is given, leaving the selection decision entirely to the agent.

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 implies read-only behavior via 'Retrieve' and specifies units (MB/hour) and granularity, but omits details like permissions, search cost, or aggregation method.

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?

Short and front-loaded: one sentence defines the tool, followed by two terse parameter explanations. No wasted words, though the 'Args:' format is slightly raw.

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?

Given the presence of an output schema and zero annotations, the description covers purpose and parameters but lacks context on when to use this tool versus related ingestion diagnostics. It is adequate but incomplete for routing in a large toolset.

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. It explains both parameters: time_span as bucket span (with examples '15m', '1h') and earliest_time as the earliest search window. The latter is vague on format but still adds meaning beyond the empty schema descriptions.

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: 'Retrieve historical ingestion throughput (MB/hour) broken down by index.' This clearly distinguishes it from most siblings, though it does not explicitly name alternatives like splunk_get_ingestion_lag.

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 purpose implies usage for monitoring ingestion volume, but there is no explicit guidance on when to use this versus splunk_get_ingestion_lag, splunk_get_parsing_errors, or splunk_index_stats. No exclusions or alternatives are named.

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 burden. The verb 'Get' signals a read operation, which is the key safety signal, but there is no statement about required permissions, whether a missing stanza errors or returns empty, or any rate/consistency caveats.

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 purpose followed by a compact Args block; nothing is padded. The args list is slightly boilerplate but each line carries semantic value the schema lacks.

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?

An output schema exists, so return values needn't be described, and both parameters are covered. However, for a tool with 70+ siblings, the absence of any routing guidance to splunk_list_inputs vs splunk_manage_input leaves a real gap in an otherwise adequate definition.

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 carry the parameters, and it does: input_type is explained with concrete examples ('monitor', 'tcp/raw', 'http', 'script') and name is clarified as 'stanza name or port'. What's missing is the relationship between the two (that name's form depends on input_type) and any case/format rules.

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: retrieve configuration and status of one input stanza, with 'specific' implicitly contrasting with splunk_list_inputs. It is clear what the tool does, but it never names the sibling tools (list_inputs / manage_input) that an agent would otherwise confuse it with.

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 by 'specific input stanza' – the agent can infer this is the single-item read versus splunk_list_inputs for enumeration. There is no explicit when-to-use, no prerequisite, and no exclusion (e.g. that manage_input should be used to change a stanza).

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