dataset_statscount, min, max, mean, median and sum of a numeric column of the Tachovo 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 full disclosure burden. It covers two nontrivial behaviors: handling of grouping commas and currency, and exclusion-and-counting of non-numeric rows. It does not describe error behavior for a missing column or the exact output shape, but the enumerated statistics imply the return content.
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 ~30-word sentence, front-loaded with the computed statistics and ending with the parsing/exclusion caveats. Every element earns its place with no 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?
The tool is simple — one required parameter, no output schema, no annotations. The description lists the six returned statistics and the key input quirks, covering most of what an agent needs to call it correctly. A precise return structure and behavior for an entirely non-numeric column are the only notable omissions.
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 add meaning to the single 'column' parameter. It clarifies that the parameter is a column name in the Tachovo dataset and that it is expected to be numeric, going beyond the bare string type. It stops short of telling the agent how to discover valid column names (e.g., via dataset_columns).
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 specific computation (count, min, max, mean, median, sum) applied to a numeric column of the Tachovo dataset, which is enough to distinguish it from siblings like dataset_columns, dataset_row, and dataset_search. The title 'Summary statistics for a numeric column' reinforces the resource and operation precisely.
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?
No sibling tool is named and no when-to-use/when-not-to-use guidance is provided. An agent can infer applicability from the enumerated statistics, but there are no explicit exclusions or alternative 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 has a distinct purpose: schema, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only possible confusion is between dataset_row and dataset_compare, since both match column values exactly, but the descriptions clarify single vs. multiple values.
All tool names share the dataset_ prefix and follow a consistent noun/feature pattern: columns, compare, provenance, row, search, stats, top. The convention is uniform and predictable.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a distinct query mode or metadata need without redundancy or bloat.
The surface covers schema discovery, provenance attribution, exact matching, free-text search, multi-value comparisons, numeric statistics, and extremes. For a read-only dataset access server, this is a complete and practical toolkit with no obvious dead ends.