dataset_topThe highest (or lowest) rows of the Defectbird 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, the description carries the behavioral burden. It discloses ordering behavior (highest/lowest) and the numeric-column constraint, which are the main traits of a top-N query. It does not address tie handling, nulls, non-numeric columns, or confirm that the operation is non-mutating, but these are less central for a ranking 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, front-loaded sentence conveys the tool's core purpose without filler. It earns a 4 rather than 5 because it buries an important clarification ('which is the most/least X') in a trailing em-dash phrase instead of making it the primary lead.
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 three-parameter, read-style ranking tool, the description covers the main intent and the numeric-column requirement, and the schema fills in parameter types and bounds. It is slightly incomplete because it does not mention output behavior or how to choose this tool from its six siblings, and there is no output schema or annotations to compensate.
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 only 33% (only ascending has a schema description), so the description must compensate. It does add meaning by describing the column parameter as numeric and by linking highest/lowest to ordering direction, but it provides no guidance on the limit parameter or the default sort direction beyond what the schema already states.
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?
Description states a specific operation — returning the highest or lowest rows of the Defectbird dataset ordered by a numeric column — and the parenthetical 'which is the most/least X' reinforces the ranking intent. It does not explicitly contrast with sibling tools such as dataset_stats or dataset_row, so it falls short of full differentiation.
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 'which is the most/least X' framing implies this tool is for top-N/bottom-N analytic questions, and 'numeric column' implies a prerequisite. However, there is no explicit statement of when to prefer this over dataset_stats, dataset_search, or dataset_row, nor any exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a distinct type of access: schema, provenance, exact row lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare are somewhat related, but the descriptions make the intended use clear.
All tools use a consistent dataset_ prefix followed by an operation noun or verb such as columns, compare, search, stats, and top. The naming pattern is predictable and makes the tool purpose easy to infer.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct data access need without redundancy or bloat.
The set covers schema discovery, provenance, exact lookup, substring search, comparisons, statistics, and top/bottom ordering, which covers most dataset Q&A workflows. There is no general-purpose filter or pagination tool, but the provided operations form a coherent query surface.