dataset_statscount, min, max, mean, median and sum of a numeric column of the Scopedly dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses handling of non-numeric rows and formatting, which is good. Yet it does not clarify the exact structure of the output (e.g., whether excluded count is included, or the order of statistics), so some behavior remains opaque.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that packs all necessary details—what is computed, input handling, and edge-case behavior—without fluff. The parenthetical clarifications are efficient and relevant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple statistical tool, the description covers the core behavior, input format, and edge cases. It lacks an explicit output schema, but the listed statistics imply the output. Given the minimal parameter set, the description is sufficiently complete for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'column' is described as a numeric column, adding semantic detail beyond the schema's plain string type. It implies that the column must contain numeric data and hints at input formatting (commas, currency). However, it does not specify whether the column is referenced by name or index, leaving minor ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the function (computing summary statistics for a numeric column) and lists the specific statistics (count, min, max, mean, median, sum). It is distinct enough as a statistical aggregation tool, though it does not explicitly contrast with sibling tools like dataset_top or dataset_search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides some usage guidance: handling of grouping commas and currency, and exclusion/counting of non-numeric rows. However, it does not explicitly state when to use this tool over others, leaving some ambiguity for an agent deciding between statistics and other dataset operations.
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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Each tool targets a distinct dataset operation, though dataset_row, dataset_search, and dataset_compare all retrieve rows via different matching semantics. The descriptions clearly separate exact equality, substring containment, and multi-value ordering, so an agent can select correctly.
All tool names follow a consistent dataset_<noun> pattern with snake_case throughout. The naming makes the tool family immediately recognizable and predictable.
Seven tools is a well-scoped set for a dataset exploration server. Each tool covers a necessary operation—schema, provenance, lookup, search, comparison, stats, and top rows—without redundancy.
The set covers the core dataset exploration lifecycle well: schema discovery, provenance, exact and fuzzy retrieval, statistics, and ranking. A minor gap is the lack of a general sample/random row tool, but existing operations are sufficient for most dataset questions.