dataset_statscount, min, max, mean, median and sum of a numeric column of the Weekrota 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 transparency burden. It discloses specific behaviors: handling grouping commas and currency, and excluding non-numeric rows. This gives useful insight into edge-case handling, though it does not mention errors, performance, or exact output format.
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 packs essential information without fluff. It is well-structured and front-loads the main function.
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?
Given there is no output schema, the description sufficiently explains what the tool returns (the six statistics) and how it handles data quirks. It lacks details about the exact output container (e.g., JSON object) but that is not strictly required without an output schema. Overall it is complete enough for an agent to understand the tool's purpose and behavior.
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 already fully describes the single 'column' parameter (type string, minLength 1). The description adds no extra meaning about the parameter itself—the note about commas/currency applies to the data values, not the parameter. Since schema coverage is 100%, baseline 3 is appropriate.
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 tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column, which is specific and distinguishes it from sibling tools like dataset_row or dataset_search. The verb is implied rather than explicit, but the intent is 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 mentions the Weekrota dataset but provides no guidance on when to use this tool versus alternatives. It does not explain when statistics are needed versus retrieving raw rows or columns, so usage context is limited.
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 different aspect of the dataset: schema, provenance, exact lookup, substring search, multi-value comparison, aggregation, and ordering. dataset_compare and dataset_row are somewhat similar since both filter on column values, but the descriptions clarify that compare handles multiple values while row handles a single exact match.
All tools follow the same dataset_ prefix convention with a lowercase noun or verb suffix: columns, compare, provenance, row, search, stats, top. The naming is uniform and predictable, making it easy to infer the purpose of each tool.
Seven tools is a well-scoped set for dataset exploration and querying. Each tool covers a distinct operation type, and none are redundant or unnecessary.
The set covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparison, descriptive statistics, and top/bottom ordering. A minor gap is the lack of a tool to list distinct values or facets, but the core needs for answering questions about this dataset are well covered.