dataset_topThe highest (or lowest) rows of the Depreo 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 carries the full burden of behavioral disclosure. It states that rows are ranked highest or lowest, but it does not disclose behavior around limit defaults, tie handling, null values, or whether this is a pure read operation with no side effects.
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 sentence that front-loads the core behavior and adds a helpful usage gloss. Every word contributes, with no repetition of schema details or filler.
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 3-parameter read tool, the description plus schema gives a usable core: choose a numeric column, optionally set a limit and sort direction. But with no output schema and no annotations, it leaves ambiguity around the default limit, tie handling, and the exact row fields returned.
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?
With only 33% schema description coverage, the description adds useful meaning by specifying that the column must be numeric and by framing highest/lowest ordering. However, the limit parameter is not explained beyond schema constraints, and no extra detail is given about default limit behavior or output shape.
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 states that the tool returns the highest or lowest rows of the Depreo dataset by a numeric column, phrased as 'which is the most/least X'. This is a specific verb-and-resource description that identifies the ranking behavior. It does not explicitly distinguish itself from sibling tools, but the operation is distinct enough from search, stats, and row retrieval.
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 a ranking use case with 'which is the most/least X' but gives no explicit guidance on when to use this tool versus alternatives. No sibling tool is named, and there is no when-not-to-use or prerequisite 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 are clearly distinct: schema, provenance, exact row lookup, search, stats, top-N, and comparison each serve different purposes. The only mild ambiguity is between dataset_compare and dataset_row, since both retrieve rows by column values, though descriptions clarify ordered multi-value vs exact single-value use.
All tools share a consistent dataset_ prefix and snake_case convention, making them easy to recognize as part of one set. However, the suffix mixes nouns like columns, row, and stats with verbs like compare and search, so it does not follow a uniform verb_noun pattern.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct common operation—schema, provenance, lookup, search, stats, top-N, and comparison—without unnecessary bloat or redundancy.
The tool surface covers the core dataset exploration workflow well: schema, provenance, exact and fuzzy lookup, numeric statistics, top-N ranking, and row comparisons. Minor gaps like arbitrary aggregation or pagination exist, but they are not critical for typical dataset questions.