dataset_topThe highest (or lowest) rows of the Longtailo 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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It says highest/lowest by numeric column, but does not state whether the operation is read-only, how ties are handled, what happens with non-numeric values, what the default limit is, or what the returned row shape looks like.
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, front-loaded sentence with no filler. It loses one point because it partly restates the title and uses a somewhat decorative em-dash phrase rather than adding fully independent behavioral detail.
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 tool with no output schema and no annotations, the description plus schema are minimally adequate for an agent to select and invoke the tool. Missing context includes defaults, handling of invalid columns, and expected return format, which would improve call correctness.
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%, so the description must compensate. It adds meaning for 'column' by requiring a numeric column and explains 'ascending' via highest/lowest ordering, but it offers no explanation of 'limit', whose semantics are left to the schema's 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 states a specific verb, resource, and scope: return the highest or lowest rows of the dataset by a numeric column. The clarifying phrase 'which is the most/least X' makes the intent unmistakable and distinguishes it from sibling tools like dataset_row or dataset_stats.
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 the use case: answer top/bottom numeric ranking questions. However, it does not explicitly mention when to prefer this tool over alternatives such as dataset_search or dataset_stats, nor does it state any exclusions.
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 targets a distinct query mode—schema, provenance, exact match, search, comparison, stats, and top-N—so there is little real ambiguity. The main possible confusion is between dataset_row and dataset_compare, since both filter by column values, but the descriptions clarify that compare is for ordered multi-value lookups while row handles single exact-value matches.
All tool names follow a consistent dataset_<noun> pattern with lowercase snake_case, making the family easy to recognize and predict. Though the names use nouns rather than verbs, the convention is uniform and clear.
Seven tools is a well-scoped set for querying and analyzing a single dataset. Each tool covers a distinct data-access need without redundancy or unnecessary bloat.
The surface covers schema discovery, provenance, exact and fuzzy lookup, multi-value comparisons, numeric stats, and top/bottom selection, which is comprehensive for typical dataset questions. A minor gap is the lack of categorical frequency counts or a way to retrieve all rows without a filter, but agents can work around those by combining existing tools.