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Depot MCP by usefulapi

Get organization usage

depot_get_usage
Read-only

Get the organization's Depot usage over a window: container builds per project (build count, minutes saved, minutes billed), GitHub Actions jobs per repo/workflow/runner (minutes elapsed and billed), storage, and agent sandboxes. Depot: UsageService/GetUsage.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_atYesWindow end, RFC 3339 (e.g. 2026-09-30T00:00:00Z).
start_atYesWindow start, RFC 3339 (e.g. 2026-09-01T00:00:00Z).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, which is already implied by 'Get'. The description adds real behavioral value by disclosing what the aggregation covers (build counts, minutes saved vs. billed, elapsed vs. billed minutes, storage, sandboxes), helping the agent understand the result set without an output schema. It stops short of stating aggregation granularity, timezone handling, or permission scope.

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?

The core sentence is front-loaded and dense, with each enumerated category earning its place by clarifying the return scope. The trailing 'Depot: UsageService/GetUsage.' is internal implementation detail that adds little for an agent caller, a minor deduction from an otherwise tight definition.

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 no output schema, the burden of describing return content falls on the description, and it does so reasonably well by listing the usage dimensions. It lacks detail on aggregation granularity or response structure, but is otherwise complete for a two-parameter read-only reporting tool.

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 description coverage is 100% and both parameters are documented in the schema with RFC 3339 format and examples. The description adds only the concept of a 'window', which is already conveyed by the start_at/end_at schema descriptions, so the baseline of 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?

The description states a specific verb (Get) and resource (organization's Depot usage) and enumerates the exact breakdown dimensions returned: container builds per project, GitHub Actions jobs per repo/workflow/runner, storage, and agent sandboxes. This granular listing makes it easily distinguishable from point-lookup siblings like depot_get_build or depot_gha_get_analytics.

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?

Usage is implied by the scope (org-wide usage reporting over a start/end window), which implies a reporting/billing use case, but the description never states when to choose this over siblings such as depot_gha_get_analytics or depot_list_builds, nor any exclusions or prerequisites. Adequate but leaves routing 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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