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Funnel Report

funnel_report
Read-onlyIdempotent

Analyze a conversion funnel and find the biggest drop-off. PREMIUM (license).

Typical input {"stages": {"Visited": 1000, "Signed up": 200, "Paid": 50}} returns {"steps": [{"from": "Visited", "to": "Signed up", "conversion_pct": 20.0, "lost": 800}, ...], "overall_conversion_pct": 5.0, "biggest_dropoff": {...}, "recommendation": "..."}.

Use when stage counts descend through one funnel. Not for retention over time (cohort_retention) and not for two-variant comparisons (ab_test). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "need at least 2 stages"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stagesYesOrdered mapping of stage name to count, top of funnel first; at least 2 stages with non-negative numeric values, e.g. {"Visited": 1000, "Signed up": 200}.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description confirms idempotency and read-only behavior and adds valuable context about error handling: 'this tool never raises a protocol error — it returns {"error": ...}' and after correction it is always safe to retry. This additional error behavior disclosure goes beyond annotations.

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 concise yet complete, using a few sentences to cover purpose, usage, input example, output example, exclusions, error behavior, and idempotency. It is front-loaded and every sentence adds value.

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?

Given the tool's complexity (1 parameter with nested object, output schema present), the description sufficiently explains input format, output structure, error handling, and retry safety. Sibling tools are listed in context, and the description provides adequate differentiation.

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?

The schema has 100% coverage for the single parameter 'stages' with a good description. The description adds extra meaning by showing a typical input example and explaining the ordering requirement ('top of funnel first; at least 2 stages with non-negative numeric values'), which provides additional semantics beyond the schema.

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 explicitly states 'Analyze a conversion funnel and find the biggest drop-off', which is a specific verb+resource. It also distinguishes from sibling tools by stating 'Not for retention over time (cohort_retention) and not for two-variant comparisons (ab_test)', ensuring no ambiguity.

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

Usage Guidelines5/5

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

The description provides clear when-to-use guidance: 'Use when stage counts descend through one funnel', and explicit exclusions with named alternatives. It also mentions PREMIUM licensing as a constraint.

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

A4.7/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: A/B testing, cohort retention, correlation, CSV profiling, trend forecasting, funnel analysis, and growth rates. There is no overlap or ambiguity between them.

Naming Consistency5/5

All tool names follow a consistent lower_snake_case pattern with descriptive names (ab_test, cohort_retention, correlation, csv_profile, forecast_trend, funnel_report, growth_rates). The naming is uniform and predictable.

Tool Count5/5

With 7 tools, the server is well-scoped for a data analysis toolkit. It covers a range of common tasks without being too sparse or overwhelming.

Completeness4/5

The set covers key analysis tasks (A/B test, correlation, cohort retention, funnel, growth, forecast, profiling). However, it lacks a tool for continuous hypothesis testing (e.g., t-test) and basic descriptive statistics, which are minor gaps for the domain.

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