dataset_statscount, min, max, mean, median and sum of a numeric column of the Handovra 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 burden and does disclose meaningful processing: grouping commas and currency are tolerated, and non-numeric rows are excluded and counted. It could go further by describing output format or behavior on missing/empty columns, but the key preprocessing traits are present.
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 dense sentence leads with the computed statistics and adds the key parsing caveats. There is no filler or repetition of schema fields.
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 tool, the description is nearly sufficient: it lists the returned statistics and the data-cleaning behavior, and the schema defines the single input. It lacks an explicit output shape, but the enumerated statistics effectively communicate what the agent will receive.
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 a required string named `column` with no description, so the description must add meaning. It does partly: the column is expected to contain numeric values, and it may include grouping commas or currency symbols. It does not specify valid column names or how to obtain them, but the sibling dataset_columns can fill that gap.
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 resource (numeric column of the Handovra dataset) and the exact operation (compute count, min, max, mean, median, sum). This clearly distinguishes dataset_stats from siblings such as dataset_columns, dataset_top, and dataset_row.
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: for summary statistics of a numeric column. However, no explicit guidance is given about when to prefer this tool over dataset_top, dataset_columns, or dataset_search, and no exclusions are stated.
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 are clearly differentiated by operation: schema, provenance, exact match, substring search, multi-value comparison, stats, and ranking. The only minor overlaps are dataset_row vs dataset_compare for single-value lookups and the shared metadata banner in dataset_columns vs the full provenance record.
All tools share a consistent lowercase dataset_ prefix and snake_case style, making the family immediately recognizable. The suffixes mix nouns (columns, provenance, row, stats) with verbs (compare, search), so it is not a strict verb_noun pattern, but the deviation is minor and readable.
Seven tools is well within the ideal range for a read-only dataset exploration server. Each tool covers a distinct query need without redundancy, and none feels like filler.
The set covers the core data-exploration lifecycle: schema discovery, provenance, exact lookup, text search, multi-value comparison, numeric summaries, and top/bottom ranking. It lacks direct numeric range filtering or group-by, but agents can often approximate these with top, stats, or search, so the gaps are minor rather than blocking.