Set an alert rule's status. WRITE: available to any authenticated user.
status must be ENABLED (the rule is evaluated) or DISABLED (it is not).
| Name | Required | Description | Default |
|---|---|---|---|
| ruleId | Yes | The rule id (UUID) | |
| status | Yes | ENABLED or DISABLED |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that this is a WRITE operation available to any authenticated user, and explains the meaning of ENABLED vs DISABLED (rule is evaluated or not). This provides meaningful behavioral context beyond the raw operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded: it states the purpose in the first sentence, then adds the WRITE permission and status semantics. Every sentence serves a distinct purpose, with no filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers the core operation well: it states the action, the allowed status values, and their meanings. It does not mention return values or error cases, but given the tool's simplicity and the absence of an output schema, the description is reasonably 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?
The input schema has 100% coverage for ruleId and status, so the baseline is 3. The description adds value by explaining what ENABLED and DISABLED actually mean ('the rule is evaluated' vs 'it is not'), which goes beyond the schema's simple 'ENABLED or DISABLED'. This semantic enrichment justifies a 4.
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 states the tool's function: 'Set an alert rule's status.' It identifies the resource (alert rule) and the specific action (setting status), and distinguishes it from sibling tools like set_alert_rule_delivery and save_alert_rule by focusing solely on the status field.
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 guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or cases where another tool (e.g., save_alert_rule) would be more appropriate. The only context is the WRITE note and status semantics, which are more about behavior than usage timing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Several tool pairs are near-duplicates, including three deprecated aliases (add_investigation_alert_channel vs add_alert_channel, list_investigation_alert_channels vs list_alert_channels, remove_investigation_alert_channel vs remove_alert_channel) that muddy the surface. Additionally, suppress_signal and create_ignore_rule both suppress alerting via different mechanisms, which could cause misselection despite detailed descriptions.
The vast majority of tools follow a clear verb_noun snake_case pattern (create_api_test, list_issues, set_alert_rule_status). A few bare-noun tools (logs, spans, metrics) and the standalone verb correlate break the pattern slightly, but overall the naming is highly consistent and predictable.
With 52 tools, this is on the extreme end of the calibration scale. Even accounting for the broad scope of an observability platform, the count is excessive and includes several deprecated redundancies that inflate it further.
The toolset provides comprehensive CRUD/lifecycle coverage across all major domains: alert rules (create, read, update, delete, status, delivery, preview), API tests (create, read, update, delete, run history, credentials), ignore rules and suppressions, issues with digest config, investigations with claim/read, channels, credentials, and rich query tools (logs, spans, metrics, SQL, traces, correlation). No obvious dead ends or missing core operations.