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Baseline readiness

log10x_baseline

Read-onlyIdempotent

Verify reporter age, pattern-coverage stability, and anomaly status before cost-reduction runs; returns not_ready with failed gates so configure_engine or estimate_savings only proceed when ready.

Instructions

Pre-flight readiness gate for cost-reduction tools. Verifies Reporter age (default 7d), pattern-coverage stability, and absence of acute anomalies before configure_engine / estimate_savings should run. Returns structured not_ready with the specific gate(s) that failed.

Input Schema

Name	Required	Description	Default
`view`	No	Output format. Always "summary": the typed envelope. Field retained for backward-compat with callers that still pass `view: "summary"`.	summary
`horizon`	No	Lookback window for the baseline. 30d is the default and what `recommended_target_range` is calibrated against; 90d trades freshness for stability on lower-volume tenants.	30d
`destination`	No	Destination SIEM. Required to project dollar baselines and decide which top contributors are compactable. Auto-detected from `env.analyzer` when omitted; falls back to `not_ready/no_destination` if neither is available.
`environment`	No	Environment nickname for multi-env setups.
`statedDailyGb`	No	Customer-stated daily SIEM ingest in GB. When supplied, the coverage gate compares observed bytes against this number; ≥80% passes, <80% returns `not_ready/coverage_low`. Omit to skip the coverage gate (the result still surfaces observed coverage as informational).
`monthly_volume_gb`	No	What-if volume lens (forecast mode): model the environment at THIS monthly volume (decimal GB/month) instead of its measured volume. The real per-pattern shares and pattern mix are held fixed; only absolute bytes and dollars scale, by one uniform factor. Use it to project a prospect onto their own scale, or to forecast a real env after growth. Pairs with siem_lens. This is a PROJECTION: the envelope stamps volume_actual_gb vs volume_projected_gb and the scale factor, and the note points at the POC for the caller real patterns.
`effectiveIngestPerGb`	No	Customer-negotiated $/GB for the destination. When supplied, baseline dollar projections use this rate and `rate_source` resolves to `customer_supplied`. Omit to fall back to vendors.json list pricing for the resolved destination (`rate_source: list_price`); omit BOTH this and `destination` to get a percent-first envelope (`rate_source: unset`, dollar fields null).

Output Schema

Name	Required	Default
`data`	No
`tool`	Yes
`view`	No	summary
`images`	No
`actions`	No
`summary`	Yes
`warnings`	No
`truncated`	No
`next_cursor`	No
`render_hint`	No
`generated_at`	Yes
`schema_epoch`	Yes
`schema_version`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.30.8Oct 15, 2026

changedInput schema / properties / view / description

Previous value: -"Output format. Always \"summary\" — the typed envelope. Field retained for backward-compat with callers that still pass `view: \"summary\"`."New value: +"Output format. Always \"summary\": the typed envelope. Field retained for backward-compat with callers that still pass `view: \"summary\"`."

First observedv1.30.7Sep 11, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already cover readOnly/idempotent/openWorld, so the bar is lower; the description adds real value by disclosing the three gates it checks, the default 7d Reporter age threshold, and the structured not_ready failure shape with the specific failed gates.

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?

Three tight sentences, front-loaded with the tool's role, then the checks, then the failure contract. No filler and nothing repeated from structured fields.

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

Completeness4/5

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

With an output schema present and full schema description coverage, the description needn't explain return fields; it still completes the picture by naming the gates and the not_ready envelope. Slightly short on what a passing result yields, but adequate for a read-only gate.

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 description coverage is 100% and each parameter (horizon, destination, statedDailyGb, monthly_volume_gb, effectiveIngestPerGb) is richly documented in-schema. The description adds no parameter-level meaning beyond that, which is the expected baseline when the schema does the heavy lifting.

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?

States a specific verb+resource: a 'pre-flight readiness gate' that verifies Reporter age, coverage stability, and anomaly absence. It explicitly positions itself relative to siblings by naming estimate_savings (and configure_engine) as downstream consumers, so an agent can distinguish it without opening a schema.

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

Usage Guidelines4/5

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

Gives a clear sequencing rule: run before configure_engine / estimate_savings. It stops short of stating explicit when-not-to-use conditions or what to do when the gate fails beyond returning not_ready, but the usage context is unambiguous.

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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