Compute an API test's uptime percentage over a window, plus latency percentiles and run counts. Uptime = SUCCESS / (SUCCESS + FAILURE); ERROR and MISSED runs are excluded. Defaults to the last 24 hours when no window is given.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | End of the window, ISO-8601 instant; defaults to now | |
| from | No | Start of the window, ISO-8601 instant; defaults to 24h before 'to' | |
| apiTestId | Yes | Id of the API test to report on |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral transparency. It notably discloses the exact uptime formula and explicitly excludes ERROR and MISSED runs, which is valuable beyond the tool's name. Although it does not explicitly state 'read-only', the verb 'Compute' strongly implies a non-mutating operation, and the formula detail adds genuine transparency.
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 three sentences, each earning its place: the first states the core purpose, the second defines the metric precisely, and the third clarifies the default window. It is front-loaded with the key action and resource, 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?
Given the moderate complexity, full schema coverage, and absence of an output schema, the description is complete. It names the primary outputs, provides the exact calculation rule, and explains how window parameters behave by default. An agent has enough information to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already fully documents all three parameters. The description adds value by confirming the default window behavior ('Defaults to the last 24 hours'), but this largely reiterates what the parameter descriptions already state. Thus, it meets the baseline without significantly surpassing 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?
The description begins with a specific verb ('Compute') and identifies the resource ('an API test's uptime percentage') along with additional outputs (latency percentiles, run counts). It clearly distinguishes this aggregated metrics tool from siblings like get_api_test_runs by focusing on uptime calculation rather than raw runs.
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 states clear context: it computes uptime over a window and defaults to the last 24 hours when no window is provided. It does not explicitly name alternatives or exclusions, but the scope is evident enough for an agent to know when aggregation is needed versus other test operations.
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.