dataset_statscount, min, max, mean, median and sum of a numeric column of the Attestroom 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 and no output schema, the description carries the full disclosure burden. It does disclose two useful behaviors—grouping commas/currency are normalized and non-numeric rows are excluded—which signals that messy input will not cause an error. However, it is silent on output format, behavior for empty/all-non-numeric columns, and error handling for unknown column names, and 'counted' is ambiguous about whether excluded rows appear in the count or are reported separately.
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 packed sentence with no filler; the statistic list is front-loaded and every clause (normalization, exclusion, counting) carries information. This is an efficient use of the available space with zero waste.
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 one-parameter tool, the core calling information is present: which column to pass and what data-cleaning behaviors to expect. Gaps remain because there is no output schema, so the return shape is only implied by the statistics list, and error conditions (missing column, no numeric values) are unstated, potentially surprising an agent at the edge cases.
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%, so the description must clarify the sole parameter. It adds real meaning: the column is expected to be numeric, may contain grouping commas or currency symbols, and non-numeric rows are dropped. It stops short of giving examples of accepted formats and does not point the agent to dataset_columns for discovering valid 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 enumerates exactly which statistics are returned (count, min, max, mean, median, sum) and binds them to a specific resource ('numeric column of the Attestroom dataset'), making the operation concrete. The verb is implied ('computes') rather than explicitly stated, and sibling tools are not named, but the specific statistic list makes confusion with dataset_columns, dataset_row, or dataset_top unlikely.
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?
No explicit when-to-use or when-not-to-use guidance is given, and no alternative sibling is named. The numeric-column emphasis and the note about currency/grouping handling imply this is the tool for column-level aggregates, but the agent must infer its boundary with siblings like dataset_top or dataset_row on its own.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, and top/bottom queries. dataset_row and dataset_compare overlap somewhat since both filter on column values, but the descriptions clarify exact single-value matching versus ordered multi-value comparison.
All tools consistently share the dataset_ prefix and use lowercase snake_case, which makes the set feel unified. However, suffixes are a mix of nouns (columns, provenance, row, stats) and verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a distinct common operation without feeling padded or redundant.
The set covers the essential dataset operations: schema discovery, provenance, exact match, text search, numeric statistics, ranking, and multi-value comparison. Minor gaps exist such as pagination for search results and range-based numeric filters, but agents can generally answer common questions without dead ends.