dataset_topThe highest (or lowest) rows of the Intakez 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 annotations absent, the description carries the burden of behavioral disclosure; it does state the core ordering behavior (highest/lowest) and the numeric-column requirement. However, it does not mention default limit behavior, tie-handling, nulls, or the exact response shape, leaving some behavioral uncertainty.
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 tightly-written sentence that front-loads the core behavior and uses a memorable intent phrase ('most/least X'). Every word earns its place and there is no redundant restating of the schema.
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 ranking tool this is close to sufficient, but with no output schema and no annotations the agent is left guessing about the default row count, the exact return shape, and edge-case ordering. The description plus schema enable a basic call, but not fully confident invocation in all common phrasing.
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 only 33%, and the description compensates partly by specifying that the column must be numeric and that ascending/descending maps to lowest/highest. It adds no explicit semantics for the limit parameter, whose default and effect on row count are left to inference from its name and schema bounds.
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 identifies the operation: return the highest or lowest rows ranked by a numeric column, and frames it as the 'most/least X' question. This precise ranking function is visually and semantically distinct from siblings like dataset_stats, dataset_search, and dataset_row, so an agent can select it without opening schemas.
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 quoted intent 'which is the most/least X' provides a clear trigger for when to call this tool, and 'highest or lowest rows' tells the agent the expected query shape. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5.
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 targets a distinct aspect of the Intakez dataset: schema, provenance, exact lookup, substring search, aggregation, ordering, and multi-value comparison. The boundaries are clear and the descriptions specify when to use each.
All seven tools share the dataset_ prefix and use concise lowercase suffixes, creating a predictable pattern. Although some suffixes are nouns and some verbs, the convention is uniform and easy to infer.
Seven tools is a well-scoped set for dataset exploration: schema, metadata, row retrieval, search, stats, top values, and comparisons are all covered without redundancy.
The surface covers the full read-only exploration lifecycle for a dataset: understand schema and provenance, filter rows, aggregate numeric columns, and rank values. No obvious dead ends for typical dataset questions.