dataset_statscount, min, max, mean, median and sum of a numeric column of the PunchListWorks 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 disclosure burden. It adds non-obvious details: grouping commas and currency are handled, and non-numeric rows are excluded and counted. This is valuable beyond the schema, though output format is not described.
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 sentence front-loads the full list of returned statistics and then appends edge-case handling. There is no filler or repetition of the schema.
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 one-parameter tool with no output schema, the description names all returned metrics and the relevant data-cleaning behavior. The only minor gap is ambiguity about whether 'count' refers to included numeric rows or excluded non-numeric rows.
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 description coverage is 0%, so the description must compensate. It does so by clarifying that the column parameter must reference a numeric column and by explaining preprocessing behavior around commas, currency, and non-numeric values.
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 tool computes count, min, max, mean, median, and sum for a numeric column, which distinguishes it from sibling tools like dataset_search, dataset_row, or dataset_compare. The resource (PunchListWorks dataset) and scope (numeric column) are explicit.
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 clearly signals when to use it: when you need summary statistics for a numeric column. It does not explicitly contrast with sibling tools, but the numeric-column restriction is a strong usage context that prevents obvious misuse.
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 distinct: columns/provenance/stats/top serve different purposes, while row, search, and compare all retrieve rows but with different matching semantics. dataset_compare and dataset_row could be confused at first glance, but the descriptions clarify exact vs. contains vs. ordered multi-value comparisons.
All seven tools share the consistent dataset_ prefix followed by a short, meaningful descriptor (columns, compare, provenance, row, search, stats, top). Even though some suffixes are nouns and some verbs, the pattern is highly predictable and uniform.
Seven tools is a well-scoped size for a read-only dataset exploration server. Each tool covers a distinct useful operation without bloat or significant redundancy.
The surface covers schema discovery, provenance, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking, which suits a read-only dataset. Missing operations like group-by or distinct-value summaries are minor and not critical for the apparent purpose.