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Glama

RooQuiz

get_form_funnel

Read-only

Read the conversion funnel for a form in the current team over the last N days, from the form_sessions telemetry: overall stages (viewed → started → submitted → leadCaptured → reportViewed → ctaClicked → shared), per-channel funnel (by utm_source, with embedded flag), UTM combos, and drop-off points (which question unsubmitted sessions stalled on). Use this to find where respondents drop and improve conversion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLook-back window in days, default 30, max 180
formIdYesThe form UUID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLook-back window actually used
formIdNoThe form this funnel belongs to
dropOffNoWhere unsubmitted sessions gave up
overallNoStage counts: { viewed, started, submitted, leadCaptured, reportViewed, ctaClicked, shared }
channelsNoFunnel split by channel
utmCombosNoFunnel split by UTM combo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly and non-destructive behavior, so the description doesn't need to echo those. It additionally discloses what data is used ('form_sessions telemetry'), what breakdowns are computed, and the drop-off analysis, going beyond the annotations and leaving no surprising behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one dense sentence with no filler. It front-loads the tool's core purpose, then adds only high-value specifics: telemetry source, overall stages, channel breakdown, UTM combos, and drop-off points. Every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema, a complete input schema, and annotations covering read-only safety, the description provides the remaining operational context: the exact funnel event stages, per-channel and UTM dimensions, and the prescribed use case. An agent has everything needed to decide and call it correctly.

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

Parameters4/5

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

Since the schema already fully documents the parameters (formId and days with defaults and limits), the baseline is high. The description adds meaningful scope by saying 'for a form in the current team' and 'over last N days', tying the parameters to the intended semantic context without repeating schema fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action 'Read the conversion funnel for a form', names the telemetry source, and pinpoints the scope ('current team', 'last N days'). It also lists the exact analytics dimensions (channels, UTM, stages, drop-off points), making it obviously distinct from siblings like get_form or get_form_stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The explicit closing guidance 'Use this to find where respondents drop and improve conversion' gives a clear when-to-use context. Exclusions or direct alternative names are not stated, but the description is enough for an agent to select it for funnel diagnostics.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the form, question, and translation families. The main ambiguity is between the lead/record tools: list_leads/get_lead vs list_records/get_record both describe entities as 'leads' even though one is the CRM lead and the other is the submission record. update_form vs update_form_settings is also a minor naming overlap, but the descriptions resolve it.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern: get_*, list_*, create_*, update_*, delete_*, add_*, set_*, etc. Even paired image upload tools follow the same convention with prepare_/finalize_. There is no camelCase or mixed verb-style chaos.

Tool Count2/5

48 tools is far above the 25-tool threshold for a heavy tool surface. While the server covers a broad platform (forms, translations, leads, bookings, examinees, tenants, images), the sheer number will burden an agent's tool-selection step. Each tool may earn its place, but the overall set is too large for easy navigation.

Completeness4/5

The toolset provides solid lifecycle coverage for forms, questions, translations, leads, bookings, examinees, and tenant administration. Minor gaps exist, such as no lead deletion/export and no way to create or delete examinees, but agents can work around these for the core quiz-and-CRM workflow. The form/translation/question CRUD surface is especially thorough.

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