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by setluca

Read the funnel

luca_analytics_funnel
Read-onlyIdempotent

Retrieve funnel stage counts, conversion rates, and period-over-period deltas by period and channel. Filter by workspace to analyze pipeline performance and identify drop-offs.

Instructions

Pipeline funnel stage counts with period and channel filters. Returns counts and conversion rates for each funnel stage plus period-over-period deltas.

Luca operation: GET /api/analytics/funnel 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.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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 the read-only/idempotent/destructive profile, and the description supplements it with genuinely useful context: required scopes (analytics:read, luca:read), data sensitivity (redacted), the underlying GET operation, and default-workspace override behavior. 'Does not require idempotency' is slightly redundant against idempotentHint=true but not contradictory.

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?

Purpose and return shape are front-loaded in the first sentence, followed by a compact metadata block. It is well organized with no wasted prose, though the trailing metadata lines are status boilerplate rather than decision-relevant.

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 detailed in prose, and the annotations carry the safety profile. The description still names the return payload and documents auth/scoping, leaving little an agent needs to call it correctly missing.

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% – the two workspace parameters carry descriptions while the nested period/channel properties show only enums with no prose. The description names 'period and channel filters', bridging part of that gap, but adds no format or default meaning beyond the schema.

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?

First sentence states a specific resource (pipeline funnel stage counts) plus the filters and returns (counts, conversion rates, period-over-period deltas). Clear and actionable, but it does not explicitly differentiate from sibling analytics tools such as luca_analytics_rollup or luca_analytics_deep_dive that could plausibly overlap.

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 statement of when to use this tool versus the many other analytics tools. The only usage-adjacent line is the workspace override note, which concerns parameters, not tool selection. An agent is left to infer fit from the name alone.

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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