Log10x MCP Server

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Metric overlay

log10x_metric_overlay

Read-onlyIdempotent

Overlay an anchor and candidate timeseries on the same aligned bucket grid to verify a suspect metric side-by-side, returning raw peaks plus deterministic facts for manual interpretation.

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

TableJSON 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

TableJSON 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.

First observedv1.30.7Sep 11, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint/openWorldHint/idempotentHint, so the safety profile is covered. The description adds real behavioral context beyond that: bucket size is auto-selected from window length and both series are right-aligned to the same trailing grid so a sparse anchor and dense candidate compare on the same chaos window. It doesn't discuss truncation/cost limits explicitly, but that is covered by max_buckets in the schema.

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?

Front-loaded with the step's role in the investigation flow, then output facts, then the exclusion ('No Pearson, no tier'), then usage. Every sentence carries signal. Slightly dense, but no filler sentences.

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?

For a 12-parameter, multi-backend tool the description conveys the return shape, alignment mechanics, and single-suspect usage pattern; an output schema exists so return values need not be fully re-explained. It is complete enough for correct invocation, though auth/backend override parameters are left to schema.

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

Parameters4/5

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

Schema description coverage is 83%, so most parameters are already documented. The description nonetheless adds cross-parameter meaning the schema lacks: bucket size auto-selection from window length and the shared right-alignment grid. It also reinforces the single-candidate design, which the schema hints at only via 'candidates[0]'.

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 and resource ('return aligned anchor + candidate timeseries plus deterministic facts'), names the concrete outputs and explicitly what it is not ('No Pearson, no tier — pure raw data'). This cleanly distinguishes it from the sibling log10x_rank_by_shape_similarity without needing to open either schema.

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?

Gives 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.' The alternative and the sequencing condition are both named, 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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