dataset_statscount, min, max, mean, median and sum of a numeric column of the Hreflangly 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, the description carries the full burden of behavioral disclosure. It usefully states that grouping commas and currency formatting are handled and that non-numeric rows are excluded and counted, which are real behavioral traits beyond the basic 'compute stats' idea. It does not describe output structure or edge cases like an all-non-numeric column, but for a read-only summary tool it provides substantive transparency.
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 sentence that front-loads the computed metrics and places parsing caveats in a parenthetical. Every part adds information, and there is no filler or redundant restatement of the tool name.
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?
The tool has no output schema, a single parameter, and no annotations, so the description must stand mostly alone. It names all output metrics and important parsing behaviors, which is fairly complete for a simple aggregation tool. Minor gaps remain around response shape, error handling for empty or fully non-numeric input, and how to discover valid column names.
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 input schema only says 'column' is a minLength-1 string, and schema description coverage is 0%, so the description is the only source of parameter meaning. It conveys that the parameter refers to a column in the Hreflangly dataset and implies it should be numeric or numeric-like. However, it does not explicitly explain how to specify the column, valid column names, or what happens if the column cannot be parsed numeriucally.
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 operation as computing summary statistics and specifies the resource: a numeric column of the Hreflangly dataset. It enumerates the exact metrics returned (count, min, max, mean, median, sum), which makes the purpose concrete. It does not explicitly differentiate itself from sibling tools, but the set of statistics makes it reasonably distinguishable from dataset_row, dataset_top, and 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 does not say when to use this tool versus alternatives such as dataset_top, dataset_search, or dataset_columns. There is no mention of preferred scenarios, exclusions, or what kinds of questions this tool answers better than its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, top values, and multi-value comparison. The only mild ambiguity is between dataset_row and dataset_compare, since both return rows filtered by column values, and between dataset_columns and dataset_provenance, since both mention provenance.
All tool names consistently use the dataset_ prefix and snake_case, which makes the set feel cohesive. However, the suffix style is mixed: some are nouns like columns, provenance, row, and stats, while others are verbs like compare and search.
Seven tools is well within the ideal range for a dataset-focused server. Each tool covers a distinct analytical need without redundancy, so the count feels intentional and appropriately scoped.
The toolset covers schema discovery, provenance, exact row lookup, free-text search, targeted comparison, numeric statistics, and extreme-value ranking. For a read-only dataset querying server, this is a complete and practical surface with no obvious dead ends.