dataset_statscount, min, max, mean, median and sum of a numeric column of the Orgplanly 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 disclosure burden. It reveals that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which adds meaningful behavioral context beyond the title.
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?
A single sentence that front-loads the returned statistics and appends important handling caveats. Every phrase earns its place with no redundancy.
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 covers the returned statistics and key edge-case behavior. It omits error handling for invalid column names, but overall it is sufficiently complete for the tool's complexity.
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, adding meaning to the otherwise undocumented parameter. It doesn't provide exhaustive detail, but it gives the essential constraint.
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 enumerates the exact statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Orgplanly dataset. This clearly distinguishes it from sibling tools like dataset_top or dataset_search.
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 tool's scope evident, but it does not explicitly state when to use this tool over siblings or mention exclusions. Usage context is implied rather than directly guided.
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 maps to a distinct query mode—schema, provenance, exact match, substring search, ordered multi-value comparison, numeric stats, and top/bottom ranking—so an agent can generally choose based on question type. The only mild overlap is between dataset_row and dataset_compare for single-value lookups, but the wording clarifies exact equality versus ordered multi-value matching.
All seven tools share the dataset_ prefix and consistent snake_case, making the family instantly recognizable. The suffix varies between noun-like and verb-like forms, but the pattern remains predictable and readable across the whole set.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a necessary operation without redundancy, and none feel superfluous or missing.
The toolset covers the full range of common data exploration needs for the Orgplanly dataset: schema discovery, provenance attribution, exact lookups, substring search, comparisons, numeric summaries, and ranked extremes. For a read-only dataset server, there are no obvious dead ends or significant gaps.