dataset_statscount, min, max, mean, median and sum of a numeric column of the HeadcountDesk 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?
With no annotations provided, the description carries the behavioral disclosure burden. It usefully reveals that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This adds meaningful behavior beyond the simple title.
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 one well-structured sentence with the core output list front-loaded and edge-case handling in parentheses. Every element earns its place with no redundancy or filler.
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 single-parameter tool with no output schema, the description fully covers what the agent needs: which column to pass, what statistics are returned, and how non-numeric data is treated. Nothing essential for a correct invocation is missing.
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?
Schema coverage is 0%, so the description must compensate. It tells the agent the sole parameter 'column' refers to a numeric column in the HeadcountDesk dataset and implies it must be a valid existing column. However, it doesn't specify exact accepted values, case sensitivity, or how to discover valid columns.
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?
Description names the operation ('count, min, max, mean, median and sum') and the resource ('numeric column of the HeadcountDesk dataset'). It clearly distinguishes this from sibling tools like dataset_row or dataset_search by focusing on aggregate statistics.
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 clearly communicates that this tool is for computing summary statistics on a numeric column, which is a distinct use case from the listed siblings. It doesn't explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.
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 maps to a distinct query type: schema discovery, provenance, exact matches, substring search, value comparison, numeric stats, and top/bottom rows. The potential overlap between dataset_row and dataset_compare is mitigated by the explicit X-vs-Y ordering purpose of compare.
All tools share the consistent dataset_ prefix and snake_case, making the family immediately recognizable. However, suffixes mix nouns (columns, row, stats) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped set for interacting with a single read-only dataset. Each tool covers a distinct user need without redundancy or overwhelming breadth.
The dataset surface is complete for the apparent purpose: schema, provenance, exact lookup, full-text search, comparison, summary statistics, and ranked extremes are all available. No obvious query type relevant to this domain is missing.