dataset_statscount, min, max, mean, median and sum of a numeric column of the Hydrantly dataset (grouping commas and currency are handled; non-numeric rows are excluded and counted).
| Name | Required | Description | Default |
|---|---|---|---|
| column | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It meaningfully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted, which is valuable beyond the title. It does not mention read-only status or edge cases like empty columns, but the disclosed parsing behavior is a strong positive.
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 compact sentence that front-loads the primary functionality and adds important parsing/exclusion details in a parenthetical. There is no redundant or filler text; every phrase adds information.
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 one-parameter tool with no output schema, the description is largely complete: it enumerates the computed statistics and key data-cleaning behaviors. Minor gaps remain around edge cases such as all-non-numeric columns or nonexistent column names, but these are not critical for basic selection and invocation.
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 schema has only a 'column' string parameter with no description, and schema description coverage is 0%. The description clarifies that the column must be numeric and that values like commas/currency are parsed, but it does not specify how column names should be provided, whether they are case-sensitive, or what values are valid.
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 identifies the resource (Hydrantly dataset) and the exact set of statistics computed (count, min, max, mean, median, sum) for a numeric column. It is specific about what the tool does, though it lacks an explicit verb and does not differentiate itself from sibling tools like dataset_top or dataset_compare.
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 intended use is implied: use this when you need summary statistics for a numeric column. However, there is no explicit guidance about when to prefer this over sibling tools, nor any exclusions or alternative recommendations.
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 data access pattern: schema, provenance, exact row lookup, fuzzy search, comparisons, top/bottom rows, and numeric stats. There is mild overlap between dataset_row and dataset_compare since both filter by column values, but their intended use cases are clearly differentiated.
All tools share a consistent dataset_ prefix and use snake_case, which makes the set look cohesive. However, the suffixes mix nouns (columns, row, stats, top, provenance) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a meaningful access mode without unnecessary redundancy or overwhelming the agent.
The tool set covers the core data exploration lifecycle: schema discovery, metadata, exact lookup, search, comparison, top/bottom ranking, and summary statistics. It lacks arbitrary multi-condition filtering or grouped aggregations, but those are not clearly required for the stated dataset Q&A purpose.