dataset_topThe highest (or lowest) rows of the Patientvo 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 burden. It discloses the core behavior (ranking by numeric column, returning top/bottom rows) but omits details like return format, default limit, or error handling. It does not contradict any annotations, and the behavior stated is accurate, though sparse.
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 no wasted words. It states the essential operation and use case efficiently. The structure is optimal for quick agent scanning.
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?
The tool is simple, but with no output schema, the description should clarify the return type (e.g., a list of rows) and any limits or defaults. It does not mention pagination, output structure, or behavior when the column is non-numeric. The missing details could lead to incorrect assumptions about the result format.
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 low (33%—only 'ascending' has a description). The description adds minimal parameter insight: it hints that 'column' must be numeric but does not explain 'limit' or 'ascending' defaults or behavior. Given the low coverage, the description fails to compensate, leaving agents without sufficient parameter guidance.
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 title and description clearly state that the tool ranks rows by a numeric column, returning the highest or lowest values. It is distinct from siblings like dataset_stats (which likely aggregates) or dataset_row (which fetches a single row), though it does not explicitly name alternatives. The verb 'rank' is specific and the resource (Patientvo dataset rows) is identified.
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 usage for 'which is the most/least X' but does not provide explicit guidance on when to use this tool versus alternatives like dataset_stats or dataset_search. There are no exclusions or comparative context. The intended use case is inferable but not clearly articulated.
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, search, exact row lookup, multi-value comparison, stats, top/bottom, and provenance. Some slight overlap exists between dataset_row and dataset_compare for single-value filters, but the descriptions generally make the intended use clear.
All tools share a consistent dataset_ prefix and use snake_case, which makes them predictable. The suffixes are a mix of result nouns and action verbs, but this does not create confusion because the prefix dominates the naming pattern.
Seven tools is a well-scoped set for a single-dataset query server. Each tool serves a distinct analytical need without unnecessary redundancy, making the surface easy to navigate.
The tool set covers schema exploration, provenance, exact lookup, text search, group comparison, summary stats, and top/bottom ranking. Minor gaps like pagination or distinct-value listing are workable around with existing tools, so the core domain is well covered.