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runmeter_summary

Read-onlyIdempotent

Aggregate LLM telemetry into cost and reliability rollups, grouping by model or other criteria to compare spend, latency, and error rates across runs.

Instructions

Aggregate telemetry into cost and reliability rollups.

group_by is one of: 'model', 'agent', 'finish_reason', 'day', or 'tag'. Optional since limits the window (ISO timestamp or relative like '30d'). Each group reports run count, total input/output tokens, total cost, average latency, and error rate. Groups are sorted by total cost descending.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNo
group_byNomodel

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already mark the tool as read-only and idempotent. The description adds valuable behavioral details: group_by allowed values, since accepting ISO or relative formats, sorting by total cost descending, and the exact metrics reported per group. No contradictions are present.

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?

The description is compact and front-loaded: purpose first, then parameter details, then output behavior. Every sentence adds relevant information without any fluff or repetition.

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 an output schema present and strong annotations, the description nevertheless covers purpose, parameters, and output metrics in sufficient detail. Minor semantics like timezone are not specified but are not critical for this simple aggregation tool.

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 0% and parameters are plain strings, but the description fully compensates by listing all valid group_by values and explaining the since format (ISO timestamp or '30d'). This adds meaning well beyond the schema, though defaults are already present in the 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 opens with 'Aggregate telemetry into cost and reliability rollups,' which uses a specific verb and resource, and distinct output details. This clearly separates it from sibling tools like runmeter_get, runmeter_list, and runmeter_export, which are not aggregation tools.

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

Usage Guidelines3/5

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

The description implies usage for aggregate summary queries but never explicitly contrasts it with alternatives or states when not to use it. There is no mention of sibling tools or exclusions, so the guidance is implied rather than explicit.

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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