dataset_statscount, min, max, mean, median and sum of a numeric column of the Sacristo 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 does disclose useful quirks: grouping commas and currency are parsed, and non-numeric rows are excluded and counted. However, the phrase 'excluded and counted' is ambiguous (whether count includes non-numeric rows), and there is no mention of error behavior or return 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?
A single sentence conveys the core function, the exact statistics, and the notable data-cleaning behaviors. It is front-loaded with the most important information and contains 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?
Given the tool's small parameter surface and simple statistical role, the description covers the essential inputs, behaviors, and outputs. It lacks an explicit output-structure description since there is no output schema, but listing the computed statistics is sufficient for an agent to infer expected results.
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?
There is only one parameter, 'column', and schema description coverage is 0%, so the description must compensate. It does this by stating that the parameter references a numeric column of the Sacristo dataset and by explaining how value formatting is handled. It stops short of providing examples or explicit valid-value guidance, but for a single string column name this is largely 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 explicitly names the resource ('Sacristo dataset') and the exact statistics computed (count, min, max, mean, median, sum), making the tool's purpose clear. It does not explicitly differentiate from sibling tools by name, but its statistical scope is distinct from row, search, and top tools.
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 phrase 'numeric column' implies the appropriate use case, and the exclusion of non-numeric rows hints at what kinds of data are acceptable. However, there is no explicit guidance about when to choose this tool over siblings like dataset_row, dataset_search, or dataset_top.
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 target clearly distinct operations—schema, provenance, search, stats, top, and exact lookup. Minor overlap exists between dataset_compare and dataset_row, both filtering rows by column values, but the descriptions clarify that compare handles multiple values while row is for a single exact match.
All tools follow a uniform dataset_<operation> pattern with consistent snake_case naming. The suffixes are a mix of nouns and verbs, but the prefix and format are fully predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct query need without redundancy.
The toolset covers schema discovery, provenance, exact and fuzzy matching, multi-value comparison, numeric statistics, and top/bottom rankings—good coverage for a read-only dataset exploration server. A possible minor gap is lack of a distinct-values or category-listing tool, but common analytical questions appear addressable.