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SetLuca

SetLuca

Official
by setluca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Read revenue

luca_analytics_revenue
Read-onlyIdempotent

Calculate revenue attribution and plan ROI for a selected period, returning total revenue, ROI, and a per-channel breakdown.

Instructions

Revenue attribution and plan ROI for a period. Returns total revenue, ROI, and a per-channel revenue breakdown.

Luca operation: GET /api/analytics/revenue Required scopes: analytics:read Required OAuth scope: luca:read; data sensitivity: redacted Does not require idempotency. Use workspaceId or workspaceSlug to override the default workspace configured by environment.

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": "luca_analytics_revenue"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryNo
workspaceIdNoOptional Luca workspace id override.
workspaceSlugNoOptional Luca workspace slug override.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.4.2
    • addedOutput schema / properties / result / properties / wonDeals
      Added value: +{
+  "maximum": 9007199254740991,
+  "minimum": -9007199254740991,
+  "type": "integer"
+}
  2. First observedv0.4.0

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive, closed-world behavior, and the description still adds meaningful context: required scopes (analytics:read, luca:read), redacted data sensitivity, and that no idempotency key is required. The note about falling back to the environment-configured default workspace is genuinely useful behavioral detail not present in the schema.

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 purpose and return values are front-loaded in the first sentence, followed by clearly separated operational metadata. The 'Luca operation: GET ...' and idempotency lines are somewhat boilerplate but each carries distinct information an agent may need.

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?

An output schema exists, so return-shape explanation is optional, and auth scopes plus workspace-override behavior are stated. The main remaining gap is guidance on choosing this tool over the many adjacent analytics tools.

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 67% schema coverage, the description adds value by explaining that workspaceId/workspaceSlug override an environment-configured default workspace. However, it says nothing about the nested query object's 'channel' filter or about period semantics beyond 'for a period', leaving part of the schema's meaning to inference.

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 names a specific verb and resource ('Revenue attribution and plan ROI for a period') and enumerates the returned metrics (total revenue, ROI, per-channel breakdown). It is distinguishable from analytics siblings such as benchmarks or funnel, though it never explicitly contrasts itself with them.

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 when-to-use or when-not-to-use guidance and no routing to alternatives like luca_analytics_rollup or luca_analytics_content_attribution. The only guidance offered is about workspace override parameters, which is invocation mechanics rather than tool selection.

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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