dataset_statscount, min, max, mean, median and sum of a numeric column of the Xlifflane 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 full burden. It discloses handling of grouping commas/currency and exclusion/counting of non-numeric rows, which adds value. However, it does not explicitly state that the operation is read-only, mention permissions, or describe error behavior for invalid columns, 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?
The description is a single, dense sentence that lists the statistics upfront and adds two relevant handling notes. No waste, and the most important information is 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?
For a simple one-parameter tool with no output schema, the description adequately conveys the output by enumerating the computed statistics. It also covers edge cases like formatting and non-numeric rows. It could mention return format or error handling, but overall it is sufficient for an agent to use the tool correctly.
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 coverage is 0%, so the description must compensate. It does by specifying the parameter is a 'numeric column' of the dataset, clarifying that the string must be an existing column name with numeric data. This adds meaningful semantic context beyond the schema's generic string type.
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 Xlifflane dataset. It uses a specific verb and resource, and the title reinforces the purpose. It is clearly distinct from siblings like dataset_columns or dataset_row.
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 its use for statistical summaries but does not explicitly state when to choose it over alternatives or when not to use it. No sibling tools are mentioned, so the agent must infer the appropriate context from the tool's purpose alone.
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 distinct purpose: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/lowest rows. The main potential confusion is between dataset_row and dataset_compare, since both handle exact value matching, but the multi-value ordering intent of dataset_compare keeps them separable.
All tools share a consistent dataset_ prefix followed by a clear operation or noun: columns, compare, provenance, row, search, stats, top. The naming pattern is uniform and predictable.
Seven tools is a well-scoped set for querying and exploring a single dataset. Each tool covers a distinct need without redundancy, and the count feels neither sparse nor bloated.
The set covers schema inspection, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom ranking. A minor gap is the lack of a tool to retrieve all rows or page through large result sets, but the existing tools are sufficient for most dataset questions.