dataset_statscount, min, max, mean, median and sum of a numeric column of the EmployeeDeskHQ 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 behavioral disclosure burden and does so well: it reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted. This gives the agent useful insight into the computation, though it doesn't specify the output structure or error handling.
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 sentence that front-loads the computed statistics and packs edge-case behavior into a parenthetical. Every part adds value, and there is no filler or redundancy.
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 single-parameter statistics tool with no output schema, the description covers the input requirements, the dataset scope, the returned statistics, and key data-cleaning behaviors. The only notable gap is the exact return format, but the listed statistics largely substitute for it.
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?
Schema description coverage is 0% and the only parameter 'column' has no schema description. The description compensates by specifying that the column must be numeric and belong to the EmployeeDeskHQ dataset, which is essential for correct invocation.
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 EmployeeDeskHQ dataset), a specific verb (compute summary statistics), and enumerates the exact outputs (count, min, max, mean, median, sum). It is readily distinguishable from siblings like dataset_search or dataset_top.
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 statistics list and the dataset name, but there is no explicit statement about when to choose this tool over sibling tools. Alternatives are not mentioned and no exclusions 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 are clearly distinct, but dataset_row, dataset_search, and dataset_compare have overlapping filtering behavior that could lead an agent to pick the wrong one. Schema, provenance, stats, and top are unambiguous.
All tools share the dataset_ prefix with snake_case, which is easy to follow. However, the suffixes mix nouns (columns, row, stats) and verbs (compare, search), so it is not a fully consistent verb_noun convention.
Seven tools is a well-scoped set for a single-dataset query server. Each tool covers a distinct mode of exploration without unnecessary redundancy.
The surface covers schema discovery, provenance, exact matching, full-text search, comparison ordering, numeric stats, and top/bottom rows. A minor gap is the lack of a general paginated 'all rows' or arbitrary aggregation tool, but common dataset questions are well supported.