dataset_statscount, min, max, mean, median and sum of a numeric column of the Stagenix 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 and does add meaningful detail: grouping commas and currency are handled, and non-numeric rows are excluded and counted. It stops short of describing the exact output shape or edge cases like an all-non-numeric column, but the disclosed parsing behavior is valuable.
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 front-loads the operation and output list, with behavior caveats compactly in parentheses. There is no filler or 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 one-parameter read-only summary tool, the description covers the main domain knowledge an agent needs, including numeric parsing and non-numeric handling. The absence of an output schema makes the exact return shape slightly implicit, and 'count' could be read ambiguously, but the listed statistics largely serve as the contract.
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%, but the description compensates by making clear that the single 'column' parameter must be a numeric column and by noting how formatted values (commas, currency) are normalized. It does not specify whether the column identifier is a display name or internal key, but with one parameter this is sufficient.
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 operations ('count, min, max, mean, median and sum') and the explicit resource ('a numeric column of the Stagenix dataset'), distinguishing it from sibling operations like row retrieval or column listing.
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 use case is implied by naming summary statistics for a numeric column, but the description gives no explicit guidance on when to prefer this tool over siblings such as dataset_row, dataset_search, or dataset_top, and no exclusions.
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 clear, distinct purposes—schema, provenance, exact-match lookup, substring search, comparisons, statistics, and ranking. dataset_compare and dataset_row overlap somewhat since both filter by column values, but compare is explicitly for ordered multi-value 'X vs Y' queries while row is for a single exact match.
All tools share a consistent dataset_ prefix and use clear, descriptive lowercase names. Even though some are nouns (columns, row, provenance) and others are verbs (compare, search, stats, top), the pattern is uniform and predictable.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct mode of interaction—schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking—without unnecessary redundancy.
The tool surface covers the core read-only dataset exploration lifecycle well: understand schema, get provenance, retrieve rows, search, compare, compute statistics, and rank. A minor gap is the absence of a tool for fetching distinct values or arbitrary aggregations, but this is not a critical dead end for the stated purpose.