dataset_topThe highest (or lowest) rows of the Enrolvo 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 of behavioral disclosure. It does state the read-only ranking behavior and highest/lowest orientation, but it does not disclose ordering details, default limit behavior, tie handling, or null handling, which would matter for a correct interpretation.
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 concise sentence with the key idea front-loaded. It has minor redundancy with the title but contains no filler or unnecessary detail.
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 description is sufficient for a simple top-N tool: an agent knows the dataset, the numeric column requirement, and the high/low ordering. However, without an output schema, the tool still does not describe the exact return format, default limit, or edge-case behavior.
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% since only 'ascending' has a description. The description adds meaning by clarifying that 'column' must be numeric and that rows are selected by high/low values, but it does not explain the 'limit' parameter, which remains undocumented in both the schema and the description.
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 dataset ranked by a numeric column, which distinguishes it from siblings like dataset_stats and dataset_search. The title supplies the explicit 'Rank' verb, making the purpose easy to identify.
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, but there is no explicit guidance about when not to use it or how it differs from dataset_stats, dataset_row, or dataset_search. The usage context is clear enough to infer, but not stated.
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.
The tools are mostly distinct: schema, provenance, exact lookup, ordered comparison, substring search, stats, and top/bottom are separate concerns. There is minor overlap between dataset_row and dataset_compare for a single exact value, but the descriptions make the intended use cases reasonably clear.
All tools consistently use the dataset_ prefix and snake_case naming. The suffixes are mostly noun-like, with compare and search as verb-like exceptions, but the overall pattern remains predictable and easy to scan.
Seven tools is well-scoped for a read-only dataset query server. Each tool addresses a distinct class of question, and none feel redundant or unnecessary for the stated purpose.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, substring search, numeric aggregation, and ordering. More advanced operations like multi-column filters or distinct-value enumeration are missing but can often be worked around with the provided tools.