dataset_statscount, min, max, mean, median and sum of a numeric column of the Cafmlane 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 behavioral disclosure burden. It discloses that grouping commas and currency symbols are parsed and that non-numeric rows are excluded and counted. These are meaningful behavioral traits beyond a generic 'compute stats' statement.
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 one concise sentence that front-loads the computed statistics and adds the important data-cleaning caveats in a parenthetical. Every word earns its place, with no repetition of schema details.
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?
The description covers operations and edge-case behavior, but there is no output schema and the description does not state the return format or exact key names. The phrase 'non-numeric rows are excluded and counted' is slightly ambiguous about whether the count is returned separately. For a simple one-parameter tool this is a minor gap, but still incomplete.
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%, but the description compensates by explaining that the 'column' parameter must identify a numeric column in the Cafmlane dataset. It also clarifies how formatting in that column is interpreted. The exact identifier format is not specified, but with a single minLength string parameter this is sufficient.
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 a specific resource ('numeric column of the Cafmlane dataset') and lists the exact operations computed (count, min, max, mean, median, sum). This leaves no ambiguity about what the tool does and clearly differentiates it from sibling tools like dataset_search or dataset_top.
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 gives clear context: use this tool for numeric column summary statistics on the Cafmlane dataset. It also notes how non-numeric rows and formatted numbers are handled, but it does not explicitly state when not to use it or name alternative tools.
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: schema, provenance, exact match, substring search, multi-value comparison, statistics, and ranking. There is some overlap between dataset_row and dataset_compare, but the descriptions clarify single-value vs multi-value use.
All tools follow a consistent dataset_ noun pattern in snake_case. The naming clearly indicates the operation each tool performs.
Seven tools is well-scoped for a dataset exploration server. Each tool earns its place by covering a distinct query mode without unnecessary redundancy.
The set covers schema inspection, provenance, exact filtering, substring search, comparison, summary statistics, and top/bottom ranking. Missing generic list-all or group-by aggregation, but the core analytical workflows are well covered.