dataset_statscount, min, max, mean, median and sum of a numeric column of the Exitvo 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 burden of behavioral disclosure. It usefully states that grouping commas/currency are handled and non-numeric rows are excluded and counted, but it does not describe the return shape, error cases, or behavior for empty/all-non-numeric columns.
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?
One tight, front-loaded sentence that packs the statistics list and key parsing caveats without filler. 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?
The tool is simple with one parameter, but there are no annotations and no output schema. The description names the computed statistics yet does not specify the output format or resolve ambiguity around whether the count of excluded non-numeric rows is returned separately.
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 says `column` is a non-empty string. The description clarifies that this parameter refers to a numeric dataset column and explains preprocessing behavior, adding meaning beyond the bare schema for a single-parameter tool.
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 the exact operation (computing count, min, max, mean, median, sum), the resource (a numeric column of the Exitvo dataset), and the special handling of formatted numbers. This clearly distinguishes dataset_stats 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: use this when summary statistics for a numeric column are needed. However, there is no explicit statement of when not to use it or which sibling alternative to choose instead, leaving routing partially to inference.
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 has a distinct role: schema, provenance, exact row lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only minor overlap is between dataset_row and dataset_compare, but their descriptions clearly separate single-exact-match from multiple-value in-order filtering.
All tools share the consistent 'dataset_' prefix with short, readable suffixes. Most suffixes are nouns (columns, row, stats, top), while 'compare' and 'search' read as verbs, a small grammatical deviation from an otherwise uniform pattern.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a meaningful query operation—metadata, lookup, search, aggregation, sorting—without redundancy or bloat.
The surface covers core dataset workflows: understanding schema, citing provenance, finding rows by exact match or substring, comparing values, computing statistics, and identifying extremes. A minor gap is the lack of distinct-value listing or pagination, but the main use cases are well supported.