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Moltline Research Desk

by com.moltlinestudio

Stats Describe

stats_describe
Read-onlyIdempotent

Describe a numeric dataset: center, spread, quartiles, and outliers. FREE.

Typical input {"numbers": [12, 15, 14, 90, 13]} returns {"n": 5, "mean": 28.8, "median": 14.0, "std_dev": ..., "min": 12, "max": 90, "q1": ..., "q3": ..., "iqr_outliers": [90], "skew": "right (mean > median)"}.

Use as a first summary of one numeric dataset. Not for interval estimates (confidence_interval) and not for planning a study (sample_size). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "no numbers"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
numbersYesThe dataset as a list of numbers; at least 1 value.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.7/5.0
Behavior5/5

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

The description adds valuable behavioral detail beyond annotations: it states the tool is 'FREE', 'read-only and idempotent', and explains that errors are returned as object with a fix message ('never raises a protocol error'). It also provides an example output. This is transparent and consistent with 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 well-structured and front-loaded: a one-sentence summary, then example, then usage guidelines, then error handling. Every sentence is meaningful and earns its place. Despite reasonable length, it is concise and easy to parse.

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 simple tool (one parameter, schema coverage 100%, annotations, output schema present), the description is complete. It explains output, errors, usage context, and safety. No missing information critical for an agent to correctly invoke the tool.

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?

The input schema already covers the parameter 'numbers' with full description (type, minItems). The description adds an example input but does not introduce new semantic meaning beyond the schema. Baseline 3 is appropriate given schema coverage is 100%.

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's purpose: 'Describe a numeric dataset: center, spread, quartiles, and outliers.' It uses a specific verb-resource combination and distinguishes from siblings by explicitly noting what it is not for (interval estimates, sample size).

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 'Use as a first summary of one numeric dataset' and contrasts with sibling tools 'confidence_interval' and 'sample_size'. It also explains error handling and retry safety, providing clear when-to-use and when-not-to-use guidance.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: statistical planning (sample_size), description (stats_describe), interval estimation (confidence_interval), citation formatting (format_citation), and product/skill retrieval (list_products, get_free_skill, get_full_skill, get_full_product). No two tools overlap in function, and the descriptions explicitly clarify boundaries.

Naming Consistency3/5

Naming is a mix of verb_noun (list_products, get_free_skill, format_citation) and descriptive noun phrases (confidence_interval, sample_size, stats_describe). While all are readable and use snake_case, the lack of a consistent pattern (e.g., all verbs or all nouns) makes it harder to predict tool names.

Tool Count4/5

At 8 tools, the count is appropriate for the server's scope, which covers statistics, citation formatting, and product retrieval. It is not overburdened, and each tool seems justified. The number is slightly above the minimal threshold but well within a reasonable range.

Completeness2/5

The server's name 'research' suggests broader coverage, but the tool surface has notable gaps. Basic statistical tools like hypothesis tests (t-test, ANOVA), correlation, or proportion analysis are missing. The citation tool is limited to three styles. The product retrieval tools are tied to a specific product line, leaving a weak general research focus.

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