dataset_statscount, min, max, mean, median and sum of a numeric column of the Yearendo 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 full burden of behavioral disclosure. It usefully reveals that grouping commas and currency formats are handled, and that non-numeric rows are excluded and counted. This goes beyond a bare statement of what the tool computes, though it does not cover output format or all edge cases.
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. It front-loads the computed statistics, then adds the data-handling caveats, with no filler or redundant restatement of the title.
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 one-parameter tool with no annotations and no output schema, the description communicates what the tool computes, on what resource, and how messy data is handled. It is nearly complete for an agent to call it correctly, missing only explicit usage boundaries and return formatting details.
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 only defines 'column' as a non-empty string, and schema description coverage is 0%. The description adds that the column must be numeric and belong to the Yearendo dataset, helping somewhat, but it does not specify valid column names, case sensitivity, or how missing values are treated beyond non-numeric rows.
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 computation: count, min, max, mean, median, and sum for a numeric column of the Yearendo dataset. It is specific about the resource and operation, though it does not explicitly differentiate itself from siblings like dataset_top or 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?
No explicit guidance is given about when to use this tool versus alternatives, and no exclusions or sibling comparisons are mentioned. The intended use is implied by the title and description, but the agent is left to infer when dataset_stats is the right choice.
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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Most tools have clear, distinct purposes: schema, search, stats, provenance, and top/bottom comparisons are unambiguous. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by exact column values, but descriptions clarify that compare handles multiple values in a specific order.
All tools follow a consistent dataset_ prefix with clear, lowercase snake_case names. The naming pattern is predictable and easy to scan, with no mixing of styles or vague generic verbs.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common operation—schema, lookup, search, comparison, stats, top values, and provenance—without unnecessary bloat.
The toolkit covers the core read-only operations needed for exploring and querying the Yearendo dataset: schema discovery, exact match, substring search, ordered comparison, numeric stats, ranking, and attribution. Minor gaps like grouped aggregations or combined filters exist, but agents can usually work around them with existing tools.