dataset_statscount, min, max, mean, median and sum of a numeric column of the Eobify 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 behavioral burden and does well by disclosing that grouping commas and currency are handled and non-numeric rows are excluded and counted. This gives the agent useful expectations about data preprocessing. It does not describe the return format, but the listed statistics strongly imply the output fields.
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 focused sentence that front-loads the result statistics and includes relevant edge-case handling. Every part earns its place, and there is no redundant or vague wording.
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 single-parameter tool with no output schema and no annotations, the description is fairly complete: it names the input, the computed metrics, and important data-cleaning behavior. It could be more complete by describing error handling for missing columns or all-non-numeric columns, but the core calling context is sufficient.
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 compensate. It does clarify that 'column' refers to a numeric column of the Eobify dataset, but it does not provide details such as accepted column name formats or how to discover valid columns. The description adds helpful context but not full parameter documentation.
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 clearly states the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Eobify dataset. This is specific and distinguishes it from sibling tools like dataset_row, dataset_search, or dataset_top, which serve different purposes.
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 makes the intended use clear: summarizing a numeric column. It does not explicitly name alternatives or say when not to use it, but the numeric-column scope and the listed statistics imply a distinct use case among the siblings. Some explicit guidance about using dataset_columns to find valid column names could improve it.
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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Each tool has a clearly defined purpose, but dataset_row and dataset_compare overlap conceptually since both retrieve rows by column value, just with different cardinality and ordering. The other tools are clearly separated between schema, provenance, search, statistics, and top/bottom ranking.
All tool names consistently use the dataset_ prefix followed by a concise operation name in snake_case. The pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common query type, and none feel redundant or unnecessary.
The tool surface covers the main dataset exploration needs: schema, provenance, exact lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no direct count of filtered rows or grouped aggregation, but agents can work around these with existing tools.