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delimit_obs_metrics

Fetch numeric metric series from your observability backend for a given time range. Use during runtime health analysis to correlate with log data.

Instructions

Pull numeric metric series from the observability backend (Pro).

When to use: during runtime health investigation when you need numeric series (CPU, memory, request rate, error rate, latency percentiles) over a named time window. Pair with delimit_obs_logs to correlate a numeric anomaly with the underlying log lines. When NOT to use: for free-text search of log lines (use delimit_obs_logs), to read or configure alert rules (delimit_obs_alerts), or for a quick at-a-glance health rollup (delimit_obs_status).

Sibling contrast: delimit_obs_logs returns text matches; this returns numeric time series. delimit_obs_status is the rollup-summary surface; this is the raw-series surface. delimit_obs_alerts configures thresholds against these same series.

Side effects: read-only on the metrics backend and gated by require_premium — unlicensed callers receive a license payload and no query runs. On a licensed call, invokes backends.tools_infra.obs_metrics which queries the backing metrics store; no data is written, no ledger entry, no notification. The response is routed through _with_next_steps.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoMetric query name. Default "system" (general system metrics). Backend-specific values supported.system
time_rangeNoWindow like "1h", "24h", "7d". Default "1h".1h
sourceNoOptional data source override. Default None = backend default source.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description fully covers side effects: read-only, license gating, internal backend invocation, no writes or notifications. Also mentions response routing through _with_next_steps.

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

Conciseness5/5

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

Well-organized into logical sections (what it does, when/not to use, sibling contrast, side effects). Every sentence adds value; no redundancy. Front-loaded with main action.

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

Completeness5/5

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

Covers purpose, usage boundaries, behavioral details, licensing, and inter-tool relationships. Output schema exists, so return value explanation is unnecessary. Complete for an agent to decide invocation.

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

Parameters3/5

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

Schema coverage is 100% with good descriptions, so baseline is 3. Description adds no extra parameter-level information beyond context of typical metric queries (CPU, memory, etc.), but that's more about output than parameters.

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

Purpose5/5

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

Clear verb 'Pull' and resource 'numeric metric series from observability backend (Pro)'. Contrasts with delimit_obs_logs (text) and delimit_obs_status (rollup), making purpose unambiguous.

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

Usage Guidelines5/5

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

Explicit when-to-use (runtime health investigation) and when-not-to-use (log search, alert config, rollup). Names specific sibling alternatives with clear differentiation.

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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