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Moltline Data Desk

by com.moltlinestudio

Csv Profile

csv_profile
Read-onlyIdempotent

Profile pasted CSV data column by column with data-quality flags. FREE.

Reports per-column type, null rate, unique count, numeric stats (min/mean/max), and top values. Typical input {"csv_text": "name,age\nAda,36\nLin,29"} returns {"rows": 2, "columns": {"age": {"type": "numeric", "null_pct": 0.0, "unique": 2, "min": 29, ...}}, "quality_flags": ["..."], "note": "first 2000 rows profiled"}.

Use as the first look at unfamiliar tabular data. Not for testing a hypothesis (ab_test, correlation) and not for time-ordered trends (growth_rates, forecast_trend). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "delimiter must be a single character, e.g. ',' or ';'"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csv_textYesRaw CSV content including a header row, pasted as a single string; the first 2000 data rows are profiled.
delimiterNoField separator, exactly one character, e.g. "," or ";". Default ",".,

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds concrete behavioral details: error handling returns error object, safe to retry, and the 2000-row limit. No contradiction.

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, front-loaded with purpose and 'FREE'. Every sentence serves a clear purpose: example, usage guidance, error handling, safety. No unnecessary words.

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 simplicity (2 params, output schema exists, rich annotations), the description covers all needed aspects: purpose, example output, when to use, error behavior, and safety. It is fully complete for agent decision-making.

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?

Schema description coverage is 100%, so baseline is 3. The description adds value by providing a typical input example, showing the output structure, and reinforcing the 2000-row limit. This elevates it beyond the schema alone.

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 tool profiles pasted CSV data column by column with data-quality flags. It gives a concrete example and explicitly distinguishes from siblings by stating it is for first look at unfamiliar data, not for hypothesis testing (ab_test, correlation) or time trends (growth_rates, forecast_trend).

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 explicitly says when to use ('first look at unfamiliar tabular data') and when not to use ('not for testing a hypothesis... not for time-ordered trends'). It also explains error handling behavior (returns error object, never protocol error) and that it is read-only and idempotent, making retry safe.

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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