dataset_topThe highest (or lowest) rows of the MultiplesBook dataset by a numeric column — "which is the most/least X".
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| column | Yes | ||
| ascending | No | true for the lowest first; default highest first |
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 provided, the description carries the full burden of behavioral disclosure. It communicates highest/lowest ranking, but does not disclose behavior around ties, missing values, non-numeric column handling, or whether a default limit exists. This leaves important behavioral questions unanswered.
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 concise sentence with no filler. It is front-loaded with the core behavior and uses the quoted phrase to convey typical usage efficiently.
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 top-k ranking tool, the description plus schema is minimally adequate to call the tool. However, there is no output schema and no annotations, so the description should ideally state the default limit behavior and the return shape more explicitly to be fully self-contained.
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 low at 33%, but the description adds the critical meaning that 'column' must be numeric, which is not stated in the schema. The ascending parameter already has a schema description, and limit's min/max constraints are present, but the description does not add further semantics for limit or ascending.
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 title 'Rank rows by a numeric column' and description 'The highest (or lowest) rows of the MultiplesBook dataset by a numeric column' state a specific verb, resource, and selection criterion. The phrase 'which is the most/least X' clarifies the intent and distinguishes it from search/statistics siblings, though it doesn't explicitly name alternatives.
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 implies usage for ranking questions like 'which is the most/least X'. It provides a clear context cue, but it offers no explicit guidance about when not to use it or when a sibling such as dataset_stats or dataset_search would be more appropriate.
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.