Timeslice trend: counts over time per series (sparklines)

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

Annotations already declare read-only. The description adds valuable behavioral context: that aggregation operators are appended automatically, the search job is auto-deleted, and default series splitting logic. This goes beyond the safety profile provided by annotations.

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

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

Two sentences, front-loaded with purpose and key constraints, no fluff. Every sentence adds essential information. Highly efficient for a complex tool.

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

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

Given 9 parameters, no output schema, and moderate complexity, the description covers query constraints, time range options, series behavior, and output format (sparklines + counts). Lacks explicit return value details but still sufficient for correct invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful context for several parameters (e.g., 'by' parsing, 'interval' auto-sizing, time range mutual exclusivity), providing value beyond the schema descriptions.

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

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

The description clearly states the tool's purpose: showing when events happened via timeslice bucketing, counts per series, and compact sparklines. It distinguishes from siblings by framing it as a pre-reading spike/onset detector, which is specific and actionable.

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

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

The description explicitly advises using this tool 'to spot spikes and onsets before reading messages,' providing a clear use case. It also warns that queries must be plain scopes without aggregation operators, but does not name sibling tools for alternative scenarios, which would elevate to a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.