dataset_topThe highest (or lowest) rows of the Mustvo 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 explaining behavior. It states that the tool returns the highest or lowest rows by a numeric column and mentions the ascending default via the parameter description. However, it does not explicitly describe side effects, output format, or tie handling, so it is not fully transparent.
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 an em-dash use-case illustration. It is front-loaded and contains no filler, making it easy to scan.
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, and the description does not specify what is returned (e.g., full rows, only the column, or a ranked table). This is a notable gap for an agent deciding how to use the result, though the core ranking behavior is clear.
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?
The description clarifies that the column must be numeric, adding semantic value beyond the schema. However, the limit parameter is left undescribed in both schema and description, and ascending is only explained in the schema. With only 33% schema description coverage, more compensation was needed.
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 the tool ranks rows by a numeric column and returns the highest or lowest rows, with the use case 'which is the most/least X'. This distinguishes it from sibling tools like dataset_search or dataset_stats.
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 gives a natural-language use case ('which is the most/least X') but does not explicitly contrast with sibling tools or explain when to prefer this over dataset_row or dataset_stats. Some guidance is present, but alternative differentiation is missing.
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 target clearly distinct operations: schema, provenance, exact match, substring search, stats, ranking, and comparison. dataset_row and dataset_compare both filter by column values, but their stated purposes (exact lookup vs. ordered multi-value comparison) keep them mostly distinguishable.
All tools share a consistent dataset_ prefix and snake_case style, making the namespace predictable. There is minor mixing between noun-style names (dataset_columns, dataset_row) and verb-style names (dataset_compare, dataset_search), but the overall pattern is still readable.
Seven tools is well-scoped for a single-dataset exploration server. Each tool covers a distinct common query need without unnecessary redundancy.
The surface covers schema discovery, provenance, exact lookups, substring search, numeric summaries, top/bottom ranking, and multi-value comparisons. Minor gaps like combined filters or pagination beyond 50 results exist, but agents can work around them for most questions.