dataset_statscount, min, max, mean, median and sum of a numeric column of the Topcoatly 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 burden of behavior. It explicitly discloses that grouping commas and currency are handled and that non-numeric rows are excluded and counted, which adds useful context beyond the schema.
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, packed sentence with no filler. The statistics list is front-loaded and every phrase 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 one-parameter tool with no output schema, the description covers the computed statistics and data-handling nuances. It stops short of stating the exact return shape, but the listed statistics make the return type reasonably inferable.
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 has zero description coverage for the 'column' parameter, so the description must compensate. It does so partially by indicating the column must be numeric, but it does not elaborate on naming, format, or behavior for invalid 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 states a specific verb (count, min, max, mean, median, sum) and a clear resource (numeric column of the Topcoatly dataset). It is immediately distinguishable from siblings like dataset_row or dataset_search.
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 use when summary statistics for a numeric column are needed, and it clarifies the dataset context. However, it does not explicitly mention when to use this tool over alternatives or any exclusions.
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 query pattern—schema, provenance, exact match, contains search, ordered value comparison, aggregation, and top/bottom ranking. The only mild ambiguity is between dataset_row and dataset_search, but their exact-match versus contains-match descriptions make the boundary clear.
All tools share the consistent dataset_ prefix followed by a clear operation name, and all use lowercase snake_case. The pattern makes the purpose of each tool predictable at a glance.
Seven tools is an appropriate scope for a read-only dataset querying server. Each tool addresses a distinct question type without unnecessary sprawl or redundancy.
The toolkit covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, descriptive statistics, and top/bottom ranking—the core workflows for answering dataset questions. It lacks general range filtering or grouped aggregation, but these are minor gaps for the stated purpose.