dataset_statscount, min, max, mean, median and sum of a numeric column of the Carbikly 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 behavioral burden. It usefully discloses that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not mention edge cases such as an empty column or output shape, but it provides meaningful behavioral context beyond the schema.
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, dense sentence that front-loads the computed statistics and then adds the important data-handling behaviors. Every clause earns its place, with no redundant information or padding.
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 single-parameter tool, the description is largely complete: it specifies the resource, the exact statistics returned, and key preprocessing behaviors. It does not detail the output schema, but since no output schema exists, the listed statistics provide a reasonable expectation of the result. Minor gaps like behavior on missing or all-non-numeric columns are not covered, but they are not critical for basic 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 input schema has only a single 'column' string parameter with no description, so schema coverage is 0%. The description compensates by explaining that the column must be numeric and that formatting artifacts like commas and currency are normalized. This gives the agent the essential semantic constraint needed to invoke the tool correctly.
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 names the specific resource (numeric column of the Carbikly dataset) and enumerates the exact statistics computed (count, min, max, mean, median, sum). This clearly differentiates it from sibling tools like dataset_row, dataset_top, and dataset_search, making the tool's function unambiguous.
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 implies when to use the tool—when aggregate summary statistics for a numeric column are needed—but it contains no explicit guidance about when not to use it or which sibling tool to prefer (e.g., dataset_top for top values, dataset_row for raw records). The usage context is clear from the title and description, but alternatives are not discussed.
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 addresses a distinct mode of dataset access: schema, provenance, exact match, multi-value comparison, substring search, statistics, and sorting. The only mild overlap is between dataset_row, dataset_compare, and dataset_search, but their descriptions make the filtering differences clear enough.
All tools share a consistent dataset_ prefix and use snake_case, which establishes a clear pattern. The second part is not perfectly uniform—some are nouns like columns and stats, while others are verbs like compare and search—but this is a minor inconsistency.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a meaningful interaction with the dataset without feeling redundant or overwhelming.
The tool surface covers schema discovery, provenance, row retrieval by exact value, multi-value filtering, text search, numeric summaries, and top/bottom ranking. Missing features like arbitrary group-by or pagination are notable but not severe for the apparent read-only exploration purpose.