dataset_statscount, min, max, mean, median and sum of a numeric column of the Lessonvo 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 full disclosure burden. It adds valuable behavioral context: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond the schema, though it doesn't cover edge cases or error behavior.
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?
One dense sentence with no filler, front-loading the key outputs and then adding formatting/edge-case detail. Every clause earns its place.
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?
Given there is no output schema, the description usefully enumerates the returned statistics and even mentions the excluded-row count. It covers the essential behavior for a single-parameter tool, though exact return formatting is left unspecified.
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 clarifies that the 'column' parameter refers to a numeric column in the Lessonvo dataset and explains how formatted and non-numeric values are treated, but it stops short of giving examples or a precise naming convention.
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 explicitly lists the computed statistics (count, min, max, mean, median, sum) and scopes them to a numeric column of the Lessonvo dataset, so an agent knows exactly what the tool does. The distinction from siblings like dataset_top or dataset_row is clear without opening their schemas.
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 provides implied usage guidance: use this tool for summary statistics on a numeric column of the Lessonvo dataset. However, it does not explicitly state when to prefer it over alternatives or when not to use it, leaving the routing largely to inference.
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 target clearly distinct operations: schema, provenance, exact match, multi-value match, substring search, stats, and top-N. The only potential confusion is between dataset_row and dataset_compare, which both fetch matching rows but differ in single vs. multiple values — the descriptions make this distinction reasonably clear.
All tools share the consistent 'dataset_' prefix in snake_case, which is good. However, the suffix mixes nouns (columns, provenance, row, stats) with verbs (compare, search), and 'dataset_top' is cryptic while 'dataset_row' is singular despite returning rows.
Seven tools is well-scoped for a single-dataset query server. Each tool earns its place covering a distinct query type: schema, attribution, exact lookup, set membership, substring search, aggregation, and ranking.
The surface covers schema, provenance, exact/partial lookup, comparison, stats, and top-N queries well. Obvious gaps include no way to page through or list all rows, no multi-condition (AND) filtering, and no group-by counts — limitations that may force agents to work around when answering comparison questions.