dataset_statscount, min, max, mean, median and sum of a numeric column of the Depreo 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?
No annotations are provided, so the description carries the behavioral burden. It discloses data-handling behavior: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. However, it does not describe output format or error behavior, leaving some transparency gaps.
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 25-word sentence packs the core result, resource, and parsing behavior with no filler. The key statistics are front-loaded, making it easy to scan.
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 one-parameter stats tool, the description is largely complete: it names the input, output statistics, and special parsing rules. Without an output schema, the exact return structure is unspecified, but the list of statistics implies it, and no critical missing information prevents correct invocation.
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 only parameter, 'column', has zero schema description coverage; the description adds that it refers to a numeric column name in the Depreo dataset, providing essential context. Yet it omits details like column naming conventions or behavior for missing columns, so it only partially compensates for the schema gap.
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?
Description explicitly lists the statistics computed (count, min, max, mean, median, sum) on a numeric column of the Depreo dataset, establishing a clear verb+resource relationship. The title reinforces the purpose and distinguishes it from sibling tools that handle columns, search, comparison, provenance, rows, and top-values.
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 computing summary statistics on a numeric column, but it does not explicitly state when to choose this over alternatives or exclude other cases. No comparison with sibling tools is provided, so an agent must infer usage from the function name and description.
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: schema, provenance, exact row lookup, search, stats, top-N, and comparison each serve different purposes. The only mild ambiguity is between dataset_compare and dataset_row, since both retrieve rows by column values, though descriptions clarify ordered multi-value vs exact single-value use.
All tools share a consistent dataset_ prefix and snake_case convention, making them easy to recognize as part of one set. However, the suffix mixes nouns like columns, row, and stats with verbs like compare and search, so it does not follow a uniform verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct common operation—schema, provenance, lookup, search, stats, top-N, and comparison—without unnecessary bloat or redundancy.
The tool surface covers the core dataset exploration workflow well: schema, provenance, exact and fuzzy lookup, numeric statistics, top-N ranking, and row comparisons. Minor gaps like arbitrary aggregation or pagination exist, but they are not critical for typical dataset questions.