dataset_topThe highest (or lowest) rows of the Hreflangly 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 burden, and it does disclose the core behavior: it returns highest or lowest rows, orders by a numeric column, and descending/ascending direction via the 'highest/lowest' contrast. It stops short of edge behaviors like tie handling or non-numeric column errors, but those are minor for a simple read-only rank 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 sentence with no filler; the operation is front-loaded and the parenthetical 'which is the most/least X' adds user-intent context without redundancy.
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 low-complexity tool with no output schema and no annotations, the description is close to sufficient: it gives the selection basis, ordering direction, and supports limit via schema. It could add one explicit sentence that it returns the ranked dataset rows rather than an aggregate, but it is not seriously incomplete.
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% (only 'ascending' has a description). The description compensates by marking 'column' as numeric and clarifying 'ascending' as lowest-first versus highest-first, but it does not explain that 'limit' caps the number of rows returned.
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?
States a specific verb ('rank') and resource (rows by numeric column), and explicitly covers the high/low ordering with the 'most/least X' intent. This distinguishes it from siblings like dataset_stats (aggregate summaries) or dataset_row (exact row lookup).
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' phrasing implies when to use the tool for top-N questions, but it never names alternatives or exclusions. An agent comparing against dataset_stats or dataset_search gets no explicit routing guidance.
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 have clearly distinct purposes: schema, provenance, exact lookup, substring search, stats, top values, and multi-value comparison. The only mild ambiguity is between dataset_row and dataset_compare, since both return rows filtered by column values, and between dataset_columns and dataset_provenance, since both mention provenance.
All tool names consistently use the dataset_ prefix and snake_case, which makes the set feel cohesive. However, the suffix style is mixed: some are nouns like columns, provenance, row, and stats, while others are verbs like compare and search.
Seven tools is well within the ideal range for a dataset-focused server. Each tool covers a distinct analytical need without redundancy, so the count feels intentional and appropriately scoped.
The toolset covers schema discovery, provenance, exact row lookup, free-text search, targeted comparison, numeric statistics, and extreme-value ranking. For a read-only dataset querying server, this is a complete and practical surface with no obvious dead ends.