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Kiln

Kiln

by codeofaxel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_material_consumption_history

Read-only

Retrieves filament usage from completed 3D prints, aggregating consumption by material over a chosen lookback period and converting length to grams.

Instructions

Get material consumption history from completed prints.

        Aggregates filament usage per material type over the given
        window, converting filament length to grams using standard
        material densities.

        Args:
            days: Number of days to look back (default 30).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_material_consumption_history"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoNumber of days to look back (default 30). Default: 30.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.5.1
    • changedInput schema / properties / days / description
      Previous value: -"Default: 30."New value: +"Number of days to look back (default 30). Default: 30."
  2. Changed2 schema fields changedv1.5.0
    • removedInput schema / properties / days / default
      Removed value: -30
    • addedInput schema / properties / days / description
      Added value: +"Default: 30."
  3. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: only completed prints are counted, results are aggregated per material type, and grams are derived from filament length via standard material densities, signaling an approximation. It stops short of describing return shape or timezone/window boundary handling.

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 explanation is front-loaded and efficient: purpose, aggregation behavior, and conversion method appear before the Args block. The Args section duplicates the schema's parameter entry, which is mild redundancy but does not obscure the core description.

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

Completeness3/5

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

There is no output schema, so the description must hint at return values; it states that usage is aggregated per material type over the window but does not specify the response shape (e.g., per-material gram totals, list vs. map). For a single-parameter read tool this is adequate but leaves the return contract underspecified.

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?

With 100% schema description coverage and a single optional parameter, the schema already documents 'days' and its default. The description restates the same default without adding format, range, or boundary semantics. Baseline 3 applies when the schema carries the parameter burden.

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

Purpose4/5

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

The description opens with a specific verb and resource: 'Get material consumption history from completed prints,' and clarifies the aggregation unit (filament usage per material type over a window). It does not distinguish itself from close siblings such as forecast_material_consumption or get_fleet_material_summary, so an agent must infer that this is retrospective rather than predictive.

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?

'From completed prints' implicitly narrows the scope to historical data, which partially separates it from forecasting tools, but there is no explicit when-to-use or when-not-to-use guidance. No mention of which sibling to prefer for projections versus actuals.

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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