dataset_statscount, min, max, mean, median and sum of a numeric column of the Limslane 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 provides meaningful behavioral details: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. This goes beyond a simple statement of purpose and helps the agent predict how messy data will be handled.
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, information-dense sentence with the core functionality front-loaded and the data-cleaning caveats neatly appended in parentheses. No redundant or filler content is present.
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 one-parameter tool, the description covers the operation, parameter semantics, and edge-case behavior. It does not specify the output structure or error behavior when a column is missing, but the enumerated statistics give a clear picture of what will be returned.
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, but the description adds crucial semantics: the column belongs to the Limslane dataset, is treated as numeric, and its values may include formatted numbers (commas, currency) that are handled. Non-numeric row handling is also clarified, giving the parameter real meaning beyond the raw schema.
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 explicitly enumerates the computed statistics (count, min, max, mean, median, sum) and targets a specific resource: a numeric column of the Limslane dataset. This clearly distinguishes the tool from siblings like dataset_row, dataset_search, and dataset_top by stating exactly what it produces.
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 intended use is implied by the description—computing summary statistics for a numeric column—but there is no explicit guidance on when to choose this tool over alternatives. No sibling tools are mentioned, and no exclusionary conditions are given.
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 have distinct purposes: schema, provenance, exact lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. The only potential confusion is between dataset_row, dataset_compare, and dataset_search, but the exact-match vs. multi-value vs. contains semantics are clearly described.
All tools share the consistent dataset_ prefix followed by a clear, lowercase noun or verb indicating the action. The names form a predictable pattern that makes the tool set easy to navigate.
Seven tools is well-suited to a single-dataset MCP server: schema inspection, provenance, row searching, comparison, stats, and top/bottom queries each earn a place. The count is neither thin nor bloated for the scope.
The surface covers the core data-exploration lifecycle: understand schema, attribute provenance, find specific rows, compare values, compute statistics, and rank results. A minor gap is the lack of a distinct-values tool for categorical columns, but most common analytical queries are supported.