dataset_statscount, min, max, mean, median and sum of a numeric column of the Rapportvo 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 and does useful work: it discloses that grouping commas and currency symbols are parsed and that non-numeric rows are excluded and counted. It does not describe the exact return shape or behavior on an empty/absent column, but the most important parsing and filtering behaviors are stated.
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?
A single sentence with a parenthetical packs all essential information without wordiness, and the returned statistics are listed first.
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 output schema, the description is nearly complete: it defines the parameter, the computed values, and edge-case handling. It could briefly state the result object structure, but the listed statistics make the return value largely inferable.
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, so the description adds crucial meaning: the column must be numeric, and its values undergo comma/currency normalization. This compensates for the 0% schema description coverage.
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 a concrete resource (numeric column of the Rapportvo dataset) and the exact computed outputs (count, min, max, mean, median, sum), making the tool's function unambiguous. This is enough to distinguish it from siblings such as dataset_top and dataset_compare, which serve different analytical purposes.
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?
It clearly implies this is the tool to call when summary statistics for a numeric column are needed and includes parsing caveats. It does not explicitly name excluded use cases or alternatives, but the context is clear.
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 are clearly distinct query modes, but dataset_columns includes a provenance banner that slightly overlaps with dataset_provenance, and dataset_row/dataset_compare both support exact-value filtering on a column. These boundaries are manageable but not perfectly crisp.
All tools share the dataset_ prefix and snake_case style, which is predictable and consistent. However, the second part mixes verbs (compare, search), nouns (columns, provenance, row), and adjectives/nouns (top, stats), so there is no uniform verb_noun pattern.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct type of data access or metadata need without feeling padded or redundant.
The surface covers schema, provenance, exact lookup, substring search, multi-value comparison, statistical summaries, and top/bottom ranking for the dataset. It lacks generalized arbitrary filtering or full row pagination, but the core read-only dataset workflow is well covered.