dataset_statscount, min, max, mean, median and sum of a numeric column of the Eposvo 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 available, the description carries the behavioral disclosure burden. It does this well by revealing that grouping commas and currency are handled and that non-numeric rows are excluded and counted. These are non-obvious behaviors beyond the basic concept of stats, though it stops short of specifying the exact output format.
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 well-structured sentence that front-loads the main output list and packs essential edge-case behavior into a parenthetical. No words are wasted and every clause adds information.
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 the simple one-parameter interface and the absence of an output schema, the description provides a clear list of returned statistics and important parsing/exclusion behavior. It is adequate for an agent to invoke correctly, though it could further detail the exact response structure or behavior on empty columns.
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. It clarifies that the 'column' parameter refers to a numeric column of the Eposvo dataset, adding meaning beyond the bare string type and minLength constraint. However, it does not specify whether the column name must match an existing column exactly or how invalid column names are handled.
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 enumerates the computed statistics (count, min, max, mean, median, sum) and identifies the target resource as a numeric column of the Eposvo dataset. This clearly distinguishes it from sibling tools like dataset_top or dataset_search, which serve different purposes.
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 numeric column analysis by listing statistics and handling of formatted numbers, but it does not explicitly state when to choose this tool over alternatives. No exclusions or alternative references are provided, leaving the when-to-use decision mostly to inference.
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.
Most tools have clearly distinct purposes: schema, provenance, exact lookup, substring search, list comparison, summary stats, and top/bottom rows. There is mild overlap between dataset_compare and dataset_row since both return exact matches, but their use cases are described distinctly enough.
All tool names follow a consistent dataset_<operation> pattern with lowercase snake_case. While the suffixes mix nouns and verbs, the pattern is uniform and predictable across the entire set.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct common query pattern without unnecessary bloat or duplication.
The tools provide complete read-only coverage for exploring and reporting on a dataset: schema discovery, provenance, exact lookup, substring search, multi-value comparisons, numeric summaries, and top/bottom ranking. No major query pattern needed for this domain is missing.