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SetLuca

SetLuca

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by setluca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Read which content brings leads

luca_analytics_content_attribution
Read-onlyIdempotent

Pull per-content attribution metrics—leads touched, qualified, booked, and revenue—for a selected period to identify which Instagram content drives sales.

Instructions

Per-content rollup (leads touched, qualified, booked, revenue) of attributed touches for a period. Content is attributed automatically from an Instagram comment or Story reply that carries a real media id, or tagged manually; an honest empty list means no content is attributed yet, not zero performance.

Luca operation: GET /api/analytics/content-attribution 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_content_attribution"
  }
}

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. First observedv0.4.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds genuine value beyond that: how attribution is derived (Instagram comment or Story reply with a real media id, or manual tagging), the empty-list semantics, data sensitivity (redacted), and required scopes. These are behavioral facts an agent cannot get from the 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 core purpose is front-loaded in the first sentence, followed by attribution mechanics, then a compact metadata block (operation, scopes, idempotency, workspace). Mostly efficient, though the 'Does not require idempotency' line is slightly redundant against the idempotentHint=true annotation and mildly confusing.

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 values need not be spelled out. Given that, the description covers the essentials: what is computed, how attribution works, the meaning of an empty result, scopes, and workspace targeting. Only the period enum values remain unexplained.

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 67%, so the baseline is 3. The description clarifies the workspaceId/workspaceSlug purpose as an override of the environment-configured default, which adds meaning. The 'period' parameter and its enum values carry no descriptive detail in either the schema or the description, leaving a gap.

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 operation ('Per-content rollup') with the exact metrics returned (leads touched, qualified, booked, revenue) and the scope ('attributed touches for a period'). This is far more concrete than a tautology. It does not, however, differentiate itself from the close sibling luca_analytics_content_performance, which an agent would need to disambiguate against.

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 only implied by the read-analytics framing. The description does add a useful interpretation rule ('an honest empty list means no content is attributed yet, not zero performance') and workspace override guidance, but it never states when to choose this over alternatives like luca_analytics_content_performance or luca_analytics_topic_performance.

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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