dataset_topThe highest (or lowest) rows of the Hardenvo 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 present, so the description is the only behavioral disclosure. It adds ordering semantics ('highest (or lowest) rows by a numeric column') and implies a read-only query, but it does not address return shape, tie-breaking, or row-count behavior.
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 compact, one sentence plus an intent-framing quote, and the ranking concept is front-loaded. The quote is mildly redundant with the main sentence but does not waste much space.
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 three-parameter tool with no output schema or annotations, the description provides the core ranking idea but omits response format and count semantics. It is adequate for a simple ranking tool but not fully self-sufficient.
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 description enriches 'column' by specifying it must be numeric and maps 'highest (or lowest)' to the ascending flag. It adds no meaning for the required 'limit' parameter, whose semantics are left to the schema's min/max constraints.
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 operation: returning the highest or lowest rows of the dataset ordered by a numeric column. This clearly distinguishes it from sibling tools like dataset_stats (summary) or dataset_row (single row), though it does not name an alternative explicitly.
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 phrase 'which is the most/least X' implies a natural-language trigger for ranking questions. However, it does not state when to prefer this over dataset_stats or dataset_search, nor does it offer any exclusions or alternative routing.
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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Most tools have distinct purposes, but dataset_row and dataset_compare both filter rows by column value and can easily be confused; the difference between a single exact match and multiple ordered matches is subtle.
All tools share the dataset_ prefix, but the suffixes mix nouns (columns, provenance, row) and verbs/adjectives (compare, search, stats, top), so the naming pattern is not fully consistent.
Seven tools is a well-scoped set for a read-only dataset exploration API, covering the main query operations without unnecessary bloat.
The set covers schema, provenance, exact lookup, text search, statistics, top/bottom rows, and comparison queries. It is missing a distinct-values or group-by operation, but the core exploration needs are well covered.