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Metric overlay

log10x_metric_overlay

Read-onlyIdempotent

Aligns an anchor and candidate timeseries on a shared trailing bucket grid and returns raw values plus peak facts for side-by-side cross-pillar verification.

Instructions

Third step of cross-pillar investigation: return aligned anchor + candidate timeseries plus deterministic facts (peak_at, peak_offset_seconds, n_buckets_aligned). No Pearson, no tier: pure raw data for the agent to interpret. Use this once log10x_rank_by_shape_similarity has narrowed to a single suspect, or to manually verify any anchor/candidate pair side-by-side. Bucket size auto-selected from window length; both series are right-aligned to the same trailing bucket grid so a sparse anchor and dense candidate compare on the same chaos window.

Input Schema

Name	Required	Description	Default
`step`	No	Bucket step. Smaller = more samples + more cost. Default 30s.	30s
`anchor`	Yes	The anchor identity. For `log10x_pattern`: the pattern symbol_message (e.g. `error_processing_payment_$`). For `customer_metric`: a PromQL expression (e.g. `apm_request_duration_p99{service="payments"}`).
`window`	No	Time window. PromQL-style duration: `15m`, `1h`, `6h`, `24h`. Alias: `timeRange`.	1h
`candidate`	No	The candidate metric to overlay against the anchor. Must be a customer-side PromQL expression that returns a single series. Alias of `candidates[0]`: pass either form (sibling tools metrics_that_moved and rank_by_shape_similarity use `candidates` array; this tool accepts both for consistency).
`timeRange`	No	Alias for `window`.
`candidates`	No	Candidates array. Accepted for parity with metrics_that_moved + rank_by_shape_similarity. Only the first element is overlaid (metric_overlay is single-candidate by design: use rank_by_shape_similarity to rank N first, then overlay the winner).
`anchor_type`	Yes	Which side the anchor comes from. `log10x_pattern`: anchor is a 10x pattern symbol_message. `customer_metric`: anchor is a customer PromQL expression.
`environment`	No	Environment nickname (for multi-env setups).
`max_buckets`	No	Max buckets to return in the aligned output. Pre-truncates from the most recent end if window/step exceeds this.
`customer_metrics_url`	No	Per-call override for the customer metrics backend URL. Wins over LOG10X_CUSTOMER_METRICS_URL env var. Use when MCP was launched with an empty/stale URL.
`customer_metrics_auth`	No
`customer_metrics_type`	No

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.

Changed2 schema fields changedv1.30.8Oct 15, 2026

changedInput schema / properties / candidate / description

Previous value: -"The candidate metric to overlay against the anchor. Must be a customer-side PromQL expression that returns a single series. Alias of `candidates[0]` — pass either form (sibling tools metrics_that_moved and rank_by_shape_similarity use `candidates` array; this tool accepts both for consistency)."New value: +"The candidate metric to overlay against the anchor. Must be a customer-side PromQL expression that returns a single series. Alias of `candidates[0]`: pass either form (sibling tools metrics_that_moved and rank_by_shape_similarity use `candidates` array; this tool accepts both for consistency)."

changedInput schema / properties / candidates / description

Previous value: -"Candidates array. Accepted for parity with metrics_that_moved + rank_by_shape_similarity. Only the first element is overlaid (metric_overlay is single-candidate by design — use rank_by_shape_similarity to rank N first, then overlay the winner)."New value: +"Candidates array. Accepted for parity with metrics_that_moved + rank_by_shape_similarity. Only the first element is overlaid (metric_overlay is single-candidate by design: use rank_by_shape_similarity to rank N first, then overlay the winner)."

First observedv1.30.7Sep 11, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover read-only/idempotent/open-world safety, and the description adds meaningful behavior: it deliberately computes no Pearson and no tier ('pure raw data'), auto-selects bucket size from window length, and right-aligns both series to a shared trailing bucket grid. That is real disclosure beyond the structured fields, though cost/rate behavior and error handling are left unstated.

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

Conciseness4/5

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

Three dense sentences, front-loaded with the pipeline position and the return shape before the usage and alignment notes. Every sentence carries information, though the alignment clause is tightly packed and takes a second read.

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, the description needn't spell out return values, and it appropriately names only the salient facts. It gives enough workflow context (single-candidate by design, step position) for an agent to invoke it correctly, despite 12 parameters.

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 83%, so the schema already documents most parameters, including aliases like `window`/`timeRange` and `candidate`/`candidates[0]`. The description adds only the bucket auto-selection and alignment semantics, which is marginal over the schema, so the baseline 3 applies.

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 precise verb+resource: return aligned anchor + candidate timeseries with named deterministic facts (`peak_at`, `peak_offset_seconds`, `n_buckets_aligned`). It also positions itself explicitly as the third step in a pipeline, distinguishing it from `log10x_rank_by_shape_similarity`, which ranks rather than overlays.

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: 'Use this once `log10x_rank_by_shape_similarity` has narrowed to a single suspect, or to manually verify any anchor/candidate pair side-by-side.' This names both the alternative tool and the condition that selects this one, leaving nothing to inference.

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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