dataset_statscount, min, max, mean, median and sum of a numeric column of the TimeCardBook 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?
The description adds useful behavioral details beyond the bare title, noting that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. This informs the user about data cleaning behavior, though it could be more explicit about the output format 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, concise sentence that packs in the statistics computed and the key behavioral caveats. There is no fluff or redundant wording; every clause adds information.
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 tool with one parameter and no output schema, the description covers the core functionality and important edge-case behavior. It could mention whether the tool returns a single row or separate values, and whether it supports multiple columns, but overall it is sufficient for basic understanding.
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 provides only a 'column' parameter with no description. The description indicates the column should be numeric, which adds some meaning, but it does not clarify whether the column name must match exactly, case sensitivity, or how to reference columns with special characters. Given the 0% schema coverage, more parameter detail would be valuable.
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 lists the exact statistics computed (count, min, max, mean, median, sum) and identifies the resource as a numeric column of the TimeCardBook dataset. This is a specific verb+resource pairing that clearly distinguishes the tool from general data tools.
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 gives no guidance on when to use this tool versus sibling tools like dataset_top, dataset_row, or dataset_search. It does not mention appropriate use cases or conditions that would make this tool the preferred choice.
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.
Each tool addresses a distinct query pattern: schema discovery, provenance, exact-match lookup, substring search, ordered multi-value comparison, column statistics, and top/bottom row ranking. The only close pair is dataset_row and dataset_compare, but their descriptions clearly separate exact single-value equality from ordered value-list comparison.
All seven tools use the same dataset_ prefix and snake_case convention, producing a predictable and scannable set. Although suffixes mix nouns (columns, stats) and verbs (compare, search), the consistent prefix and clear semantic labels make naming highly regular.
Seven tools is well-scoped for read-only interrogation of a single dataset, covering metadata, lookup, search, comparison, statistics, and extreme values without redundancy. The count is comfortably in the ideal range for this purpose.
The surface covers the main dataset operations: schema, provenance, exact and fuzzy retrieval, comparisons, aggregates, and ranking. A minor gap is the lack of a way to retrieve all rows or list unique categorical values, but most realistic questions can be answered with the provided patterns.