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yokozuna-mcp

by mbe24
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Timeslice trend: counts over time per series (sparklines)

sumo_trend
Read-only

Groups log messages into time buckets, counts per series (auto-detected severity or custom field), and displays a sparkline to help spot spikes and trends before reading individual messages.

Instructions

Shows WHEN things happened: buckets matching messages with | timeslice, counts per bucket split into series (default: the scope's AUTO-DETECTED severity field, disclosed in the output), and renders one compact sparkline + per-bucket counts per series. Use it to spot spikes and onsets before reading messages. The query must be a plain scope — no | aggregation operators (timeslice/count are appended; jobs auto-deleted). Time range: exactly ONE of last (relative, e.g. "15m", "2h"; units s/m/h/d) OR both from and to (ISO-8601 like 2026-07-02T18:28:00, or epoch milliseconds).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
byNoSeries dimension. "_"-prefixed = native Sumo field (e.g. _sourcecategory); "none" = one total series; anything else is an ABSOLUTE JSON path from the _raw root (dots allowed — e.g. stream, log.levelname). Omitted: the scope's auto-detected severity field (disclosed).
toNoEnd time: ISO-8601 or epoch ms. Requires `from`.
fromNoStart time: ISO-8601 or epoch ms. Requires `to`.
lastNoRelative window ending now, e.g. "15m", "2h", "1d". Mutually exclusive with from/to.
queryYesSumo Logic scope query (keywords + metadata filters; no | aggregation operators).
filterNoOptional raw Sumo fragment applied between the scope and the timeslice (same contract as sumo_error_digest's filter=) — e.g. trend ONLY the errors using a fragment sumo_describe_schema proposed. With filter= AND an explicit by=, no detection runs (exactly 1 job).
intervalNoBucket size, e.g. "30s", "5m", "1h" (units s/m/h/d). Default: auto — the smallest nice step giving ≤40 buckets over the window.
timeZoneNoIANA timezone for query-time parsing (default UTC).
maxSeriesNoMax series rendered, ranked by total count (default 8; the rest merge into "(other)").
byReceiptTimeNoSearch by receipt time; recommended true for very recent windows (ingestion lag).

Tool Definition Quality

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and openWorldHint=false. Description adds that jobs are auto-deleted, timeslice/count operators are appended, and time range validation. No contradiction. Provides additional behavioral context beyond annotations.

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?

Single paragraph, relatively concise while covering purpose, usage, constraints. Front-loaded with key info. Could be slightly more structured but no wasted words.

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?

With 10 parameters, 1 required, no output schema, description explains return format (sparkline + per-bucket counts per series) and internal behavior (auto-delete, operator appending). Covers the main use case and constraints comprehensively.

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 coverage is 100% with descriptions. Description adds meaning: for `by`, explains default auto-detected severity field; for `interval`, default auto gives ≤40 buckets; for `filter`, references sibling tool's contract. Provides useful beyond schema.

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?

The description starts with 'Shows WHEN things happened' and details bucketing via | timeslice, counts per series, and sparkline rendering. It clearly distinguishes from sibling tools like sumo_get_messages or sumo_run_search by focusing on temporal aggregation and visualization.

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?

Explicitly states 'Use it to spot spikes and onsets before reading messages.' It also specifies constraints: query must be a plain scope (no | aggregation), time range must be exactly one of last or from/to. Does not explicitly mention when not to use, but context is clear.

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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