dataset_topThe highest (or lowest) rows of the Medcontra 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 provided, the description carries the full burden of behavioral disclosure. It does disclose the numeric-column constraint and the highest/lowest orientation, but it is silent on material behaviors: default limit when none is passed, handling of non-numeric or missing values, tie-breaking, and output shape. For a ranking tool these gaps are significant enough that an agent could call it and get surprising results.
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 zero filler. The core capability comes first, and the 'which is the most/least X' parenthetical earns its place by giving the agent a query-intent anchor. Nothing here is redundant or wasteful.
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?
Despite low complexity, the tool has no annotations and no output schema, so the description must carry more weight than it does. It fails to state the default limit, whether full rows are returned, or how non-numeric columns are handled, and it offers no routing guidance against overlapping siblings. A top-N ranking tool needs at least default behavior and output shape to be safely invocable.
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), so the description must compensate. It adds real value by specifying that 'column' must be numeric and by mapping highest/lowest to the ascending flag. However, 'limit' remains undocumented in both the schema and the description, and the highest/lowest semantics duplicate what the schema's ascending description already conveys.
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 identifies the resource (rows of the Medcontra dataset) and the operation (ranking by a numeric column, highest or lowest), with a natural-language mapping in 'which is the most/least X' that makes intent unmistakable. It distinguishes itself from siblings like dataset_search and dataset_stats by the ranking/ordering focus, though it names no sibling explicitly and the verb is implied rather than stated ('The highest... rows' vs. 'Returns the highest... rows').
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 only usage signal is the heuristic 'which is the most/least X', which implies when an agent might reach for this tool but offers no explicit when-to-use or when-not-to-use guidance. It never names alternatives, so nothing helps an agent decide between dataset_top and overlapping siblings like dataset_stats (aggregates) or dataset_search (matching rows).
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 pattern: schema, provenance, exact match, substring search, multi-value comparison, aggregations, and sorted top rows. While dataset_row and dataset_compare both do exact column matching, their different purposes (single value vs ordered X vs Y comparisons) are clearly described.
All tools share a consistent dataset_ prefix followed by lowercase snake_case names, making the family recognizable. There is a minor mix of noun-like names (columns, row, stats, provenance) and verb-like names (compare, search, top), but the pattern is still predictable and readable.
Seven tools is well within the ideal range and each tool provides a distinct operation for exploring a dataset. The count feels neither bloated nor thin for the server's stated purpose.
The tool surface covers schema discovery, provenance, exact lookups, substring search, multi-value comparisons, numeric statistics, and top/bottom rows—covering the main ways one would interrogate a dataset. A minor gap is the lack of a general paginated 'all rows' or arbitrary filtering tool, but the provided tools handle most realistic queries.