dataset_topThe highest (or lowest) rows of the RemitDeck 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?
No annotations are provided, so the description must carry the full behavioral burden. It states the basic sort-and-slice behavior and partially covers the ascending flag via the schema, but it does not disclose default limit behavior, tie handling, return format, or data type constraints beyond 'numeric column'.
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 sentence with no fluff, and the example phrase 'which is the most/least X' adds a practical use-case context. It is appropriately brief, though the structure is slightly informal.
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 and no annotations, the description leaves important gaps: it does not explain what the returned rows look like, what the default limit is, or how ties are resolved. For a 3-parameter, 1-required tool, this level of incompleteness is inadequate for confident 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?
With only 33% schema description coverage, the description must compensate. It adds meaning by specifying that the column must be numeric and that results are highest or lowest, which helps interpret the `column` and `ascending` parameters. However, the `limit` parameter is completely undescribed in both schema and 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 title and description together clearly state the verb (rank) and resource (RemitDeck dataset rows) and specify the key qualifier 'by a numeric column'. It distinguishes itself from siblings like dataset_search and dataset_row by framing the operation as returning the highest or lowest 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?
There is no guidance on when to use this tool versus alternatives such as dataset_stats or dataset_search. The only implicit hint is the phrase 'which is the most/least X', which suggests a use case but does not explicitly exclude sibling tools or state conditions.
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.
dataset_row, dataset_search, and dataset_compare all return rows and can overlap when querying by a simple value, but each has a distinct mode: exact match, substring, and multi-value ordered comparison. Schema and provenance tools are clearly separated from row-level queries.
All tools use the same dataset_ prefix and snake_case style, creating a predictable and recognizable family. The second element mixes nouns and verbs, but the pattern is consistent enough to cause no confusion.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool covers a distinct need (schema, provenance, filtering, comparison, statistics, ranking) without unnecessary bloat.
The surface covers schema discovery, provenance, exact/substring filtering, multi-value comparison, numeric stats, and top/bottom ranking. Missing features like group-by or distinct-value queries are minor gaps that can often be worked around with existing tools.