dataset_statscount, min, max, mean, median and sum of a numeric column of the Subbielane 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?
No annotations are provided, so the description carries the full burden. It does disclose useful behavior: grouping commas and currency are handled, and non-numeric rows are excluded and counted. However, it does not mention the return format, whether 'count' refers to total rows or valid numeric rows, or error handling for missing/invalid columns.
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, focused sentence that front-loads the computed statistics and then adds edge-case handling with no wasted words. It is appropriately sized for a one-parameter tool.
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?
With no output schema and no annotations, the description should fully define what the agent gets back. It lists the statistics but leaves ambiguity about the output structure and the exact meaning of 'count' relative to excluded non-numeric rows, so the agent still has some uncertainty before calling.
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 coverage is 0%, so the description must compensate for the undocumented 'column' parameter. It clarifies that the column should be numeric and part of the Subbielane dataset, but it does not explain exact column naming, case sensitivity, or how to discover available column names (e.g., via dataset_columns).
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 the exact statistics computed (count, min, max, mean, median, sum) for a numeric column of the Subbielane dataset, and the title reinforces this. This distinguishes it from siblings like dataset_row, dataset_top, and dataset_compare.
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?
No guidance is given about when to use this tool versus its siblings, and no exclusions or alternatives are mentioned. The only usage cue is the generic 'numeric column' context, which does not help an agent route among dataset_columns, dataset_row, dataset_top, etc.
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 targets a distinct operation on the Subbielane dataset: schema, provenance, exact row match, fuzzy search, statistics, top/bottom, and value-list comparison. Some mild overlap exists between dataset_search, dataset_row, and dataset_compare for lookups, but their matching semantics are clearly differentiated.
All tools follow the consistent 'dataset_' prefix followed by a short, descriptive noun or verb. The naming convention is uniform and predictable, making it easy to infer each tool's purpose.
Seven tools is a well-scoped number for a dataset-querying server. Each tool covers a distinct query pattern without redundancy or bloat.
The tool surface covers the core dataset exploration needs: schema, provenance, exact lookup, search, statistics, extremes, and comparisons. A minor gap is the lack of a tool to list all rows or paginate through the dataset without a filter, but most practical questions can be answered.