dataset_topThe highest (or lowest) rows of the Monthendly 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 states the sorting intent but does not reveal default limits, tie-breaking behavior, handling of missing or non-numeric values, or any other operational details beyond what can be inferred from the schema.
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 main idea front-loaded. The quoted gloss is slightly redundant, and the typo "Monthendly" adds noise, but overall it is efficient and easy to parse.
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 has no output schema, minimal annotation coverage, and a thinly described parameter set, so the description is not fully adequate for correct invocation. An agent still needs to infer the default limit, the exact effect of `ascending`, and the expected return shape.
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 for the undocumented parameters. It adds useful meaning to `column` by specifying it must be numeric, and hints at `ascending` via "highest or lowest," but provides no guidance on `limit` or how the parameters interact.
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 ranks rows of the Monthendly dataset by a numeric column to find the most or least X. It identifies the resource and the operation, though it does not explicitly differentiate from siblings like dataset_stats or dataset_search.
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 phrase "which is the most/least X" implies the tool is for top-N or bottom-N ranking queries, giving some usage context. However, it does not mention when to prefer this tool over sibling tools or provide any exclusion criteria.
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 has a distinct purpose (schema, provenance, exact match, substring search, multi-value compare, stats, top/bottom), and descriptions clearly differentiate them. The only mild overlap is between dataset_row and dataset_compare, but the exact-match vs multi-value distinction is explicit enough to avoid serious confusion.
All tools share a consistent 'dataset_' prefix in snake_case, which creates a clear family identity. However, suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is not perfectly uniform verb_noun.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query pattern without redundancy or bloat, and the count sits comfortably within the ideal 3–15 range.
The tool surface covers the core needs for working with a dataset: schema discovery, provenance, exact lookup, full-text search, comparisons, numeric aggregates, and top/bottom ordering. Minor gaps exist, such as no dedicated count-by-filter or multi-column filtering, but agents can work around these using existing tools.