Moltline Data Desk

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

Name	Required	Description	Default
`stages`	Yes	Ordered 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

Name	Required	Description	Default
No arguments

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds critical behavioral context: error handling returns a structured error object instead of protocol errors, and it explains retry safety. The 'PREMIUM (license)' note also sets usage expectations.

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 efficiently structured: purpose, example, usage guidance, and error behavior in four clear sections. Each sentence adds value, and the front-loaded purpose lets an agent quickly grasp the tool's function.

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?

For a single-parameter tool with a rich output schema, the description covers purpose, input example, usage constraints, error semantics, and licensing. It is fully self-contained for selection and invocation.

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 description coverage is 100%, so the schema already documents the 'stages' parameter. The description provides a typical input example, which is helpful but doesn't add semantic meaning beyond what the schema offers. Baseline 3 applies.

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 opens with 'Analyze a conversion funnel and find the biggest drop-off,' which uses a specific verb and resource. It further distinguishes from siblings by explicitly naming cohort_retention and ab_test as alternatives for other use cases.

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?

It gives explicit when-to-use guidance ('Use when stage counts descend through one funnel') and when-not-to-use guidance with named alternatives ('Not for retention over time (cohort_retention) and not for two-variant comparisons (ab_test)').

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

A4.8/5.0

Disambiguation5/5

Each tool addresses a distinct analytical task: A/B test, cohort retention, correlation, CSV profiling, forecasting, funnel analysis, and growth rates. There is no overlap, and the descriptions clearly state when to use each and what not to use it for.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern with descriptive noun_noun combinations (e.g., ab_test, cohort_retention, growth_rates). No mixing of conventions or vague verbs.

Tool Count5/5

7 tools is a well-scoped set for a data analysis server. It covers a range of common statistical and data profiling tasks without being overwhelming or too sparse.

Completeness4/5

The tool surface covers core analytical needs: hypothesis testing, retention analysis, correlation, data profiling, forecasting, funnel analysis, and growth rates. Minor gaps exist (e.g., no general descriptive statistics beyond CSV profiling, no regression), but the set feels intentional and sufficient for typical data desk queries.

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