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SetLuca

SetLuca

Official
by setluca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Read framework objection performance

luca_learning_framework_performance
Read-onlyIdempotent

Measure win rate by objection bucket for the configured sales framework over a rolling window, revealing correlational performance signals in SetLuca.

Instructions

Win rate by objection bucket for the sales framework the coach has configured right now, over a rolling window. These numbers are correlational — read them as a signal, not as proof a framework caused the result.

Luca operation: GET /api/framework-performance Required scopes: learning:read Required OAuth scope: luca:read; data sensitivity: full_content 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_learning_framework_performance"
  }
}

Input Schema

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

Output Schema

NameRequiredDescriptionDefault
resultYes
provenanceNo

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 cover read-only, idempotent and non-destructive behavior, but the description goes further by disclosing required scopes (learning:read, luca:read), data sensitivity (full_content), and a genuinely useful caveat that the figures are correlational, not causal. This adds real context beyond the structured hints; only the scope/OAuth/idempotency scaffolding is standard boilerplate.

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 the correlation caveat are front-loaded and earn their place, and the workspace override note is actionable. The trailing metadata block (operation, scopes, idempotency line) is somewhat listy and partly restates annotation hints, but it stays compact.

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 an output schema present, the description needn't detail return values, and it covers resource scope, windowing, workspace override and auth sensitivity. The main remaining gap is alternative-tool routing against the adjacent objection/learning 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?

Schema coverage is 67%: workspaceId and workspaceSlug are documented in-schema and the description reinforces their purpose ('override the default workspace configured by environment'). windowDays, however, is undocumented in both places — the description only implies 'rolling window' without stating the default of 30 or the 1–90 range, so the description does not fully compensate for the 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 metric (win rate by objection bucket) tied to a specific resource (the sales framework the coach has configured now) over a defined window, so the agent knows exactly what it reads. It does not, however, name or distinguish itself from conceptually similar siblings like luca_analytics_objections or luca_learning_panel.

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?

It gives interpretive guidance ('read them as a signal, not as proof') and explains the workspace override, but never says when to prefer this tool over the many adjacent objection/analytics siblings. Usage is implied rather than stated as a when/when-not rule.

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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