dataset_statscount, min, max, mean, median and sum of a numeric column of the FMlane 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 provided, the description carries the behavioral disclosure burden. It usefully states that grouping commas and currency symbols are handled and that non-numeric rows are excluded and counted. It does not describe edge cases like an all-non-numeric column, but the provided behaviors are meaningful and non-obvious.
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 sentence that front-loads the operation and output metrics, then adds relevant parsing and exclusion behavior. Every clause adds information, and there is no redundancy or filler.
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 tool with one parameter and no output schema, the description is complete enough for correct selection and invocation. It states what input is needed, what values are accepted, how data quirks are handled, and what statistics will be returned.
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 defines 'column' as a string with minLength 1, so description-level semantics are important. The description clarifies that column refers to a numeric column of the FMlane dataset and that values may include formatting like commas and currency, adding real meaning beyond the schema.
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 a clear verb and resource: it computes summary statistics for a numeric column of the FMlane dataset. It lists the exact metrics (count, min, max, mean, median, sum), which distinguishes it from siblings like dataset_row, 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 description gives clear context for when to use the tool: when summary statistics for a numeric column are needed. It does not explicitly mention alternatives or exclusions, but the unique statistical purpose is conveyed well enough that an agent can select it appropriately.
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 targets a distinct query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric aggregates, and top/bottom rows. Although dataset_row and dataset_compare both filter on column equality, their descriptions clearly separate single-value from multi-value ordered use.
All tools share a consistent dataset_ prefix and snake_case, making the family obvious. The suffix is not uniformly verb_noun, mixing nouns (columns, provenance, stats) with verbs (compare, search), so it is predictable but not perfectly consistent.
Seven tools is well-scoped for a single-dataset query server. Each tool corresponds to a common question type about the FMlane dataset, and none feel redundant or superfluous.
The set covers the core data-exploration surface: schema, provenance, exact/contains lookup, comparisons, numeric summaries, and extremes. Minor gaps exist, such as no distinct-value enumeration or grouped counts, but they can usually be worked around with dataset_compare and dataset_search.