dataset_statscount, min, max, mean, median and sum of a numeric column of the Medcontra 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 burden, and it genuinely discloses two behavioral traits: grouping commas and currency are normalized, and non-numeric rows are excluded and counted. This goes beyond a bare operation statement into real data-handling context. It stops short of covering error cases (missing column, all-non-numeric column) or return format, which would round out the behavior profile.
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 tight sentence that front-loads the statistic list and defers the handling caveats; there is zero filler. Every phrase earns its place, and the length is proportionate to the tool's simplicity.
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 one-parameter stats tool, the description covers the core operation and the main data-cleaning quirks, which is most of what an agent needs. It omits the return structure and edge-case behavior, and since there is no output schema, that information is neither delegated nor provided. The gaps are modest but noticeable for a tool an agent must call blindly.
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 description coverage is 0%, so the description must compensate, and it partially does: 'numeric column of the Medcontra dataset' tells the agent that `column` takes a column name whose values should be numeric. It does not explain how to discover valid column names or verify the column exists, and the acceptance of string-formatted numbers is only implicit through the comma/currency note.
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 the exact outputs (count, min, max, mean, median, sum) and the resource (a numeric column of the Medcontra dataset), making the tool's function unambiguous. It does not explicitly differentiate it from siblings like dataset_top or dataset_search, though the enumerated statistic list largely does so implicitly. A stated verb like 'computes' is implied rather than present.
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?
There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The numeric-column qualifier and the note that commas/currency are handled imply a use case for messy numeric data, but an agent must infer when to choose this over dataset_row, dataset_search, or dataset_top. A pointer such as 'use dataset_columns to discover valid column names' would materially improve routing.
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 pattern: schema, provenance, exact match, substring search, multi-value comparison, aggregations, and sorted top rows. While dataset_row and dataset_compare both do exact column matching, their different purposes (single value vs ordered X vs Y comparisons) are clearly described.
All tools share a consistent dataset_ prefix followed by lowercase snake_case names, making the family recognizable. There is a minor mix of noun-like names (columns, row, stats, provenance) and verb-like names (compare, search, top), but the pattern is still predictable and readable.
Seven tools is well within the ideal range and each tool provides a distinct operation for exploring a dataset. The count feels neither bloated nor thin for the server's stated purpose.
The tool surface covers schema discovery, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom rows—covering the main ways one would interrogate a dataset. A minor gap is the lack of a general paginated 'all rows' or arbitrary filtering tool, but the provided tools handle most realistic queries.