dataset_statscount, min, max, mean, median and sum of a numeric column of the Dividvo 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 behavioral disclosure. It goes beyond a generic statement by explaining that grouping commas and currency symbols are handled, and that non-numeric rows are excluded and counted. This meaningfully describes edge-case behavior.
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 front-loaded sentence lists the output statistics first and then supplies the important data-cleaning caveats in a parenthetical. Every part is useful and there is no 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?
For a simple one-parameter tool with no output schema, the description covers what is computed, the dataset scope, and how messy numeric values are handled. It could add an example or explicit output shape, but the essentials are present.
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 non-empty string, and schema coverage is 0%. The description adds that the column must be numeric and belong to the Dividvo dataset, and that formatted numeric values are tolerated. It does not specify exact naming, case sensitivity, or how to find valid column names.
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 (a numeric column of the Dividvo dataset) and exactly what the tool computes: count, min, max, mean, median, and sum. This clearly distinguishes it from siblings like dataset_columns 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 intended use is implied: call this when you need summary statistics for a numeric column. However, it does not explicitly state when not to use it or identify alternatives, such as using dataset_top for row-level exploration or dataset_compare for comparisons.
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 clearly different purpose: schema, provenance, exact match, search, stats, ranking, and comparison. dataset_row and dataset_compare overlap for single-value equality, but the descriptions make the ordered multi-value use case clear.
All tools follow the same dataset_ prefix with a descriptive noun or verb, forming a highly predictable naming pattern. There is no mixing of conventions or vague generic names.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct query mode without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact filtering, substring search, numeric statistics, top/bottom ranking, and comparisons. Minor gaps exist such as pagination or listing all rows, but agents can accomplish most dataset tasks with these tools.