dataset_statscount, min, max, mean, median and sum of a numeric column of the Hardenvo 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 does disclose that non-numeric rows are excluded and counted and that commas/currency are handled, but it does not mention read-only status, error behavior, or what happens if the column is missing or entirely non-numeric.
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, compact sentence with a useful parenthetical. It front-loads the computed statistics and avoids unnecessary filler.
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 enumerates the returned statistics and explains the data-cleaning behavior well enough for a simple tool. It does not describe the exact return shape, but no output schema exists and the listed values are sufficient for invocation.
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 only defines column as a string, and the description adds meaning by indicating it refers to a numeric column in the Hardenvo dataset. However, it does not specify whether the column must exist, case sensitivity, or behavior when given a non-numeric column.
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 names the action (computes summary statistics) and the specific outputs (count, min, max, mean, median, sum) for a numeric column. This distinguishes it from the sibling tools such as 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 gives processing details about commas, currency, and non-numeric rows but does not state when to use this tool versus the sibling alternatives. No explicit comparison or selection guidance is provided.
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 have distinct purposes, but dataset_row and dataset_compare both filter rows by column value and can easily be confused; the difference between a single exact match and multiple ordered matches is subtle.
All tools share the dataset_ prefix, but the suffixes mix nouns (columns, provenance, row) and verbs/adjectives (compare, search, stats, top), so the naming pattern is not fully consistent.
Seven tools is a well-scoped set for a read-only dataset exploration API, covering the main query operations without unnecessary bloat.
The set covers schema, provenance, exact lookup, text search, statistics, top/bottom rows, and comparison queries. It is missing a distinct-values or group-by operation, but the core exploration needs are well covered.