dataset_statscount, min, max, mean, median and sum of a numeric column of the The Water Hub 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. It discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which are important edge-case behaviors. It does not mention return format, but the list of statistics implies the result structure.
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, well-structured sentence that front-loads the statistics list and then adds relevant edge-case details. No redundant words; every clause adds value.
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 is simple with one parameter and no output schema. The description covers the core behavior, the dataset context, and key edge cases. It does not explicitly describe the return structure, but the listed statistics imply it. Overall, adequate for an agent to call it correctly.
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 zero description coverage for the single 'column' parameter. The description adds that the column must be numeric and belongs to The Water Hub dataset, but does not provide examples or specify exact naming conventions. This partially compensates for the schema gap but leaves some ambiguity.
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 states a specific action (computing count, min, max, mean, median, sum) on a numeric column of a named dataset, with explicit handling of formatting and non-numeric rows. It clearly distinguishes itself from sibling tools like dataset_columns or dataset_row by focusing on statistical aggregation.
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 indicates this is for summary statistics on a numeric column, implying use when such aggregates are needed. It does not explicitly list alternatives or exclusions, but the purpose is specific enough that an agent can infer when to use it versus other dataset tools.
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 operation on the dataset or enquiry. dataset_search, dataset_row, dataset_compare, and dataset_top have clear differences in matching logic and output. Enquiry tools are clearly separate. No two tools appear to do the same thing.
All dataset tools follow a dataset_* pattern, and enquiry tools follow enquiry_* except submit_enquiry which inverts the order. This is a minor deviation but the overall naming is predictable and clear.
10 tools is well-scoped for the domain, covering both data querying and enquiry submission without bloat.
The dataset query surface covers schema, metadata, row lookup, search, stats, and top/bottom. Enquiry covers description, fields, and submission. Minor gaps like a 'list all rows' or enquiry status check exist but are not critical.