dataset_topThe highest (or lowest) rows of the WallChartBook 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 full transparency burden, yet it only restates the ranking behavior and the numeric-column requirement. It does not disclose limit behavior, default sort direction, output format, or what happens with non-numeric columns, which an agent would need to know.
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 compact sentence that front-loads the operation and resource. There is no filler or repetition.
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?
Given no output schema, no annotations, and only one of three parameters described in the schema, the description is too thin to be complete. It omits return value shape, limit semantics, and sort-direction behavior, all of which matter for correct invocation.
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 should compensate, but it only adds the 'numeric column' constraint and a vague most/least hint. It does not explain the limit parameter, its maximum of 50, or how ascending maps to 'lowest first', leaving the agent under-informed.
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 action — return the highest or lowest rows of the WallChartBook dataset ranked by a numeric column — and gives the use-case phrase 'which is the most/least X'. This clearly distinguishes it from siblings like dataset_row (single row), dataset_search (searching), and dataset_stats (aggregate statistics).
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' phrase gives a clear context for when to use the tool, but the description does not name alternatives or state when not to use it. No comparison to dataset_stats or dataset_search is provided, so the guidance remains implied rather than explicit.
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, but dataset_row and dataset_compare overlap for single-value equality checks, and dataset_search's substring match can also return exact matches. The descriptions largely resolve this, so the ambiguity is minor.
All tool names use the same dataset_ prefix and lowercase snake_case, which is predictable and clean. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search) and the adjective-like 'top', so it is not a fully consistent verb_noun pattern.
Seven tools is an appropriate size for a dataset-query server; each tool covers a distinct query pattern (schema, exact lookup, search, comparison, stats, ranking, provenance) without redundancy or bloat.
The server covers the core read-only workflows for the dataset: schema discovery, row retrieval, text search, comparisons, numeric summaries, top/bottom rankings, and citation metadata. It lacks a way to list all rows or get distinct column values, but these are workable gaps rather than dead ends.