dataset_statscount, min, max, mean, median and sum of a numeric column of the Clientvo 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 reveals important handling behavior: grouping commas and currency are normalized, and non-numeric rows are excluded and counted. It also enumerates the exact computed values, which substitutes for the missing output schema. It does not mention error cases or assumptions about missing data, but the stated behavior is useful and non-obvious.
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, information-dense sentence with no filler. The main statistics list is front-loaded, and the parsing caveats are neatly placed in parentheses. Every part adds value.
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 simple one-parameter tool with no annotations and no output schema, this description covers the core information: what the tool computes, for which dataset, and how edge-case rows are treated. It could be slightly more complete by explicitly naming the 'column' parameter and describing the response shape, but the description already conveys most of what an agent needs.
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?
The schema provides only the parameter name 'column' with a minLength constraint and no description, so the description must add meaning. It does clarify that the column is expected to be numeric and that non-numeric rows are handled specially. However, it does not explicitly connect this meaning to the 'column' parameter or describe required formats, leaving some ambiguity about how the column is referenced.
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 clearly states that the tool computes summary statistics (count, min, max, mean, median, sum) for a numeric column of the Clientvo dataset. This is a specific verb-plus-resource description that distinguishes it from sibling tools like dataset_top, dataset_row, and dataset_search, though it does not explicitly contrast with them.
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 intended use is implied: use this tool when you need numeric summary statistics for a column in the Clientvo dataset. However, there is no explicit guidance on when to prefer this over sibling tools such as dataset_top or dataset_compare, and no 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.
Add one secure layer between your agents and this server.
Each tool maps to a clear query type (schema, metadata, exact lookup, substring search, multi-value compare, stats, top-N) with only minor overlap between dataset_row and dataset_compare for single-value filtering. The examples in the descriptions help an agent choose correctly.
All seven tools share the dataset_ prefix and use consistent snake_case with a noun/operation suffix (columns, compare, provenance, row, search, stats, top). This creates a predictable pattern that is easy to extend and select from.
Seven tools is appropriate for a dataset query server: enough to cover common exploration tasks without being overwhelming. Each tool has a distinct role, so the count feels well-scoped rather than padded.
The set covers schema discovery, provenance, exact and fuzzy lookup, comparisons, numeric aggregates, and top/bottom queries — the core operations for a single dataset Q&A server. It lacks a direct 'count rows matching a filter' or distinct-values tool, but agents can work around those with existing tools.