dataset_statscount, min, max, mean, median and sum of a numeric column of the Sowbird 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 important behaviors: grouping commas and currency symbols are handled, and non-numeric rows are excluded and counted. This goes beyond a bare 'computes statistics' statement and gives the agent insight into data cleaning and edge-case handling.
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 list of statistics, then adds two parenthetical clarifications. Every clause earns its place, and there is no fluff. It is concise and well-structured for quick parsing.
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 tool with one parameter and no output schema, the description covers the computed metrics and edge-case handling (non-numeric rows excluded and counted). It does not specify the exact return format (e.g., JSON keys) or error behavior for missing/empty columns, but it provides enough for an agent to invoke the tool and understand the result.
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, and schema description coverage is 0%. The description adds that the column is numeric within the Sowbird dataset, which helps infer intent, but it does not explain how to specify the column (e.g., exact name, case sensitivity) or what happens if the column does not exist. Some meaning is added but not enough to fully compensate for the low coverage.
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 statistics computed (count, min, max, mean, median, sum) and identifies the resource (Sowbird dataset). This is a specific verb–resource pairing that clearly distinguishes it from sibling tools like dataset_row or dataset_top, which handle raw rows or top values rather than summary aggregates.
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 usage for summary statistics but does not explicitly state when to use this tool over alternatives or provide exclusions. It names no sibling tools or conditions (e.g., 'use dataset_top for top-N values instead'). The context is clear but not explicit.
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 targets a distinct query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The descriptions clearly differentiate row/compare/search, though row and compare have some conceptual overlap.
All tool names follow the same `dataset_` prefix followed by a noun (columns, compare, provenance, row, search, stats, top), creating a predictable and consistent naming pattern.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool serves a unique purpose with no redundancy, covering schema, metadata, lookup, search, comparison, statistics, and ranking.
The set covers schema, provenance, exact and substring search, comparisons, summary stats, and extremes, which handles most dataset Q&A needs. Minor gaps like group-by aggregation or pagination are not critical for the apparent purpose.