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Kiln

Kiln

by codeofaxel
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

revenue_dashboard

Read-only

Retrieve aggregate revenue analytics for a chosen period, including total revenue, sales counts, top models, monthly trends, and marketplace breakdown.

Instructions

Get aggregate revenue analytics dashboard.

        Returns total revenue, sales count, top models, monthly
        trends, and marketplace breakdown.

        Args:
            days: Number of days to include (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": "revenue_dashboard"
  }
}

Input Schema

NameRequiredDescriptionDefault
daysNoNumber of days to include (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 include (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

B3.2/5.0
Behavior3/5

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 behavior is covered. The description usefully discloses the shape of the aggregate result, but adds nothing about auth/scope requirements, data freshness, or whether results are scoped to a user/org.

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 purpose and return contents are front-loaded in two tight sentences. The trailing Args block duplicates the schema verbatim and is the only wasted text.

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 description appropriately names the returned fields, and the single optional parameter is covered. Only the aggregation scope (per-user, per-printer, or global) is missing, which matters for interpreting the numbers.

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 the single days parameter is fully documented in the schema, so the baseline applies. The description merely restates the argument and its default rather than clarifying how the window is anchored (rolling from now vs. calendar days) or any maximum.

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 gives a specific verb and resource ('Get aggregate revenue analytics dashboard') and enumerates the returned metrics (total revenue, sales count, top models, monthly trends, marketplace breakdown). It does not, however, distinguish this from nearby siblings such as model_revenue, record_revenue, or usage_summary, so an agent has no explicit routing hint.

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

Usage Guidelines2/5

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

There is no guidance on when to use this dashboard versus model_revenue or usage_summary, and no prerequisites or exclusions are stated. The only usage-shaped information is the lookback window implied by the days argument.

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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