dataset_statscount, min, max, mean, median and sum of a numeric column of the Mashzo 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 a good job: it discloses that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. This goes beyond a generic 'compute stats' statement, though it leaves some ambiguity about how the excluded count is represented.
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 no filler: the main promised outputs are listed first, followed by two important behavioral caveats. This is appropriately sized and front-loaded.
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?
Given the low complexity, single required parameter, and no output schema, the description is largely complete for selecting and invoking the tool. It covers the computed values, numeric parsing, and exclusion behavior. The main gap is the lack of explicit return-format details, but the listed statistics make the likely output predictable.
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 the parameter name 'column' with zero description coverage. The description adds meaning by specifying that the column must be a numeric column of the Mashzo dataset, but it does not provide example values, formatting requirements, or behavior for invalid/unmatched column names.
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 states a concrete, specific operation: computing count, min, max, mean, median, and sum for a numeric column of the Mashzo dataset. It clearly distinguishes the tool from siblings like dataset_row, dataset_search, and dataset_top by focusing on statistical summaries.
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 implies usage for numeric-column summary statistics but does not explicitly state when to use this tool over alternatives, nor does it mention any exclusions. A clear context is present, but the agent is left to infer the choice versus sibling tools.
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.
The tools are largely distinct: schema, provenance, exact lookup, substring search, compare, stats, and top-k. The only mild overlap is among dataset_row, dataset_search, and dataset_compare, since they all retrieve rows, but their descriptions clarify exact match, contains, and value-list comparison respectively.
Every tool uses a consistent `dataset_` prefix with clear snake_case names. Even though some suffixes are nouns and some are verbs, the pattern is uniform and predictable across the entire tool set.
Seven tools is a well-scoped size for a dataset querying server. Each tool covers a distinct common operation without feeling redundant or excessive.
The surface covers the core dataset operations well: schema inspection, provenance, exact lookup, search, comparisons, statistics, and top/bottom ranking. Minor gaps include distinct-value enumeration and grouped aggregation, but these are not fatal for typical dataset questions.