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Kiln

Kiln

by codeofaxel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

printer_trend_analysis

Read-only

Analyze a printer's local print history to surface health scores, failure rate and duration trends, recurring failure modes, and material reliability over a chosen lookback period.

Instructions

Analyze local print history trends for a printer.

Uses only data already stored in the local database — nothing
leaves the machine.  Returns health score, failure rate trends,
duration trends, recurring failure modes, and material reliability.

Args:
    printer_name: Printer to analyze.
    lookback_days: How far back to look (default 30 days).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "printer_name": "<printer_name>"
    },
    "name": "printer_trend_analysis"
  }
}

Input Schema

NameRequiredDescriptionDefault
printer_nameYesPrinter to analyze.
lookback_daysNoHow far back to look (default 30 days).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.5.1
    • addedInput schema / properties / lookback_days / description
      Added value: +"How far back to look (default 30 days)."
    • addedInput schema / properties / printer_name / description
      Added value: +"Printer to analyze."
  2. Changed1 schema field changedv1.5.0
    • removedInput schema / properties / lookback_days / default
      Removed value: -null
  3. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds genuinely new behavioral context: the computation is entirely local ('nothing leaves the machine') and it enumerates what the analysis returns (health score, failure rate trends, duration trends, recurring failure modes, material reliability). It does not discuss cost, latency, or behavior when history is sparse.

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?

Purpose and privacy constraint are front-loaded in the first two sentences, and the return-value list is a compact enumeration. The trailing Args section duplicates the schema and is the only meaningfully redundant part.

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 only two simple parameters, no output schema, and read-only annotations, the description covers what an agent needs: what it computes, that it is offline, and what the result contains. Minor gaps are the absence of guidance for empty/short history windows and no mention of returned data volume.

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 already documented in the schema with identical text. The Args block merely restates those descriptions verbatim, adding no format, range, or default-override guidance beyond what the schema provides, so the baseline 3 applies.

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?

States a specific verb and resource: 'Analyze local print history trends for a printer.' It is clearly distinguishable from analytics-adjacent siblings like printer_stats or failure_history because it emphasizes aggregate trend output (health score, failure rate trends, duration trends). It stops short of naming an alternative sibling to route against, so a 4 rather than a 5.

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 'local print history' and 'lookback_days', which signals a retrospective aggregate query rather than a live status check. However, there is no explicit when-to-use/when-not statement and no pointer to siblings such as printer_usage_summary or get_printer_insights for overlapping questions.

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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