Skip to main content
Glama
Sign In
SetLuca

SetLuca

Official
by setluca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Read call intelligence

luca_analytics_call_intelligence
Read-onlyIdempotent

Post-call intelligence for a period: show rate, close rate, average rating and rating distribution, call-to-close lag, repeat-call cohort, day-of-week and call-sequence show rates, and surprise patterns, derived from post-call events and…

Instructions

Post-call intelligence for a period: show rate, close rate, average rating and rating distribution, call-to-close lag, repeat-call cohort, day-of-week and call-sequence show rates, and surprise patterns, derived from post-call events and coach-recorded outcomes.

Luca operation: GET /api/analytics/call-intelligence 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_call_intelligence"
  }
}

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. Changed2 schema fields changedv0.4.1
    • addedInput schema / properties / query / properties / channel
      Added value: +{
+  "enum": [
+    "telegram",
+    "instagram",
+    "messenger",
+    "whatsapp"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / query / properties / period
      Added value: +{
+  "enum": [
+    "this_week",
+    "this_month",
+    "last_week",
+    "last_month",
+    "last_30_days"
+  ],
+  "type": "string"
+}
  2. First observedv0.4.0

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds useful operational context: the underlying GET endpoint, required OAuth scopes, and redacted data sensitivity. It does not describe return values, but an output schema exists.

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 description front-loads the metric surface and then adds endpoint, scope, and workspace override details. The long metric list is dense but justified for an analytics tool, and no sentence is obviously wasteful.

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?

For a read-only analytics endpoint with annotations and an output schema, the description covers the metric surface and auth requirements well. However, it leaves query-parameter interaction and sibling selection implicit, so an agent still needs to infer some invocation details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% and the query object is nested, yet the description only mentions 'for a period' and workspace overrides. It does not explain how start and end interact with period, nor what the channel filter does, leaving the schema to carry most parameter meaning.

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 states a specific analytics resource (post-call intelligence) and enumerates the metrics it returns, so the agent can tell what data it produces. It does not explicitly distinguish itself from the closely named luca_analytics_call_intelligence_calls sibling, leaving some ambiguity about aggregate vs per-call views.

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?

The description implies usage through the metric list and period/channel context, but it never states when to choose this tool over nearby analytics siblings such as luca_analytics_funnel, luca_analytics_revenue, or luca_analytics_call_intelligence_calls. Usage is inferred rather than explicitly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools