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Kirk — Unsupervised Structural Change Detection

Get Usage Summary

kirk_billing_usage
Read-only

Return the caller's inference consumption over the last N days from the append-only Gate 2 events table.

Purpose: Historical usage summary + per-tool breakdown for the caller's account.

Use when: You need a usage report for the caller or an admin, or you are reconciling ledger debits against actual inference events.

Do not use when: You need real-time cost — the _cost envelope on every agent-driven tool result covers that inline.

Capability class(es): Meta (metering).

Path fit: MCP only.

Cost: 0 IU.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNowindow size (default 30).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds context: it is historical (not real-time), sourced from an append-only table, and useful for reconciling ledger debits. It also mentions a non-functional 'Cost: 0 IU' which adds mild context without contradicting 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?

The description is well-structured with clear sections (purpose, use when, do not use when) and front-loads the core action. The extra metadata (Capability class, Path fit, Cost) is brief and not overly repetitive, though it could be trimmed without losing essential guidance.

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?

Given the tool's simplicity (one optional parameter, read-only, with an output schema), the description fully covers what the tool does, when to use it, when not to, and the underlying data source. No critical behavioral information is missing.

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?

The single `days` parameter is fully described in the schema ('window size (default 30)') with 100% coverage. The description's 'last N days' simply echoes the schema, adding no new semantic detail beyond the default value already present.

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 begins with a specific verb and resource: 'Return the caller's inference consumption over the last N days from the append-only Gate 2 events table.' It also clarifies the scope (per-tool breakdown) and distinguishes itself from real-time cost tools via the 'Do not use when' clause.

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?

Explicit 'Use when' and 'Do not use when' sections provide clear context for when to invoke this tool versus alternatives like the `_cost` envelope on tool results. This gives direct guidance for an agent to choose correctly.

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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TDQS

A4.7/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose: billing (checkout/show/usage), scoring (single/batch/legacy/synthetic/random), utility (health/models/bulk_howto/render/research). Descriptions are detailed enough to prevent confusion.

Naming Consistency5/5

All tools follow a consistent 'kirk_<domain>_<verb>' pattern (e.g., kirk_billing_checkout, kirk_score_book). No mixing of conventions or unpredictable names.

Tool Count5/5

12 tools is well within the optimal range. Each tool serves a specific need without redundancy, covering scoring, billing, health, models, and research.

Completeness5/5

The tool surface covers the full workflow: scoring (single/batch/legacy/synthetic/research), billing (checkout/balance/usage), health checking, model listing, tensor prep, and bulk client generation. No obvious gaps for the intended domain.