dataset_statscount, min, max, mean, median and sum of a numeric column of the MultiplesBook 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 full behavioral burden and does useful work by disclosing preprocessing (commas/currency handled) and row exclusion behavior. It stops short of describing edge cases or the exact return structure, but covers the main behaviors an agent needs for this simple read-only tool.
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 dense sentence front-loads the output statistics and then adds preprocessing caveats with no redundant phrasing. Every part earns its place.
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 or annotations, the description covers what is computed, input preprocessing, and row handling, which is nearly complete. It lacks only finer edge-case details such as behavior when all rows are non-numeric or when the column is empty.
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, so the description adds the essential meaning that it must be a numeric column of the dataset. It does not enumerate valid column names or mention how to discover them, which would be needed for full compensation at 0% schema description coverage.
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 exactly what the tool computes (count, min, max, mean, median, sum) for a numeric column of the named MultiplesBook dataset. This clearly distinguishes it from siblings like dataset_search, dataset_row, and dataset_columns.
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?
It conveys that the tool applies to numeric columns and that non-numeric rows are handled, so an agent can infer when it is appropriate. However, it does not explicitly compare with sibling tools or state when a different tool should be used, leaving usage guidance implied rather than explicit.
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 target distinct query types: schema, provenance, exact row lookup, search, comparisons, stats, and top/bottom. However, dataset_row, dataset_search, and dataset_compare have some functional overlap around filtering rows, which could lead to selection mistakes.
All tools follow a consistent dataset_<operation> pattern with clear nouns describing the operation. This makes the tool set predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct common question type without unnecessary proliferation.
The surface covers the main ways users would query the dataset: schema, exact matches, substring search, comparisons, stats, and top/bottom. A minor gap is the lack of a simple 'get all rows' or pagination tool, but that is not required for the apparent Q&A purpose.