dataset_statscount, min, max, mean, median and sum of a numeric column of the Defectbird 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 full disclosure burden. It usefully discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted. However, it does not mention return format, error behavior, or handling of missing columns, leaving important behavioral gaps.
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, efficient sentence with no redundant filler. The statistic list is front-loaded, and the dataset scope and data-handling notes follow logically.
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 single-parameter tool with no output schema, the description covers scope and some edge-case behavior (commas, currency, non-numeric rows). However, it doesn't specify the output structure, so an agent may be uncertain whether the result is a flat object with keys count/min/max/mean/median/sum or something else. Error behavior is also unspecified.
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 no description for the 'column' parameter (0% coverage), so the description must compensate. It clarifies that the parameter refers to a numeric column in the Defectbird dataset, adding some meaning beyond the bare schema. Yet it omits details like naming conventions, case sensitivity, or behavior for nonexistent or non-numeric 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 explicitly enumerates the statistics computed (count, min, max, mean, median, sum) and names the target resource as a numeric column of the Defectbird dataset. This makes the tool's function clear and distinguishes it from siblings like dataset_search or dataset_row, though it lacks an explicit verb such as 'computes' or 'returns'.
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 description implies usage for numeric columns of the Defectbird dataset, providing a clear context. However, it offers no guidance on when to prefer this tool over sibling tools like dataset_top or dataset_compare, 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.
Each tool has a distinct type of access: schema, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare are somewhat related, but the descriptions make the intended use clear.
All tools use a consistent dataset_ prefix followed by an operation noun or verb such as columns, compare, search, stats, and top. The naming pattern is predictable and makes the tool purpose easy to infer.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct data access need without redundancy or bloat.
The set covers schema discovery, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom ordering, which covers most dataset Q&A workflows. There is no general-purpose filter or pagination tool, but the provided operations form a coherent query surface.