dataset_topThe highest (or lowest) rows of the Handovra 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, but it only states high/low ranking by a numeric column. It omits default limit behavior, handling of ties or missing values, and the read-only nature of the operation.
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?
A single sentence that front-loads the core behavior with no filler. It is appropriately sized and every phrase contributes meaning.
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?
For a tool with no annotations and no output schema, the description leaves out important context: the default limit, output format, and when to choose this over sibling tools. It is minimally viable but under-specified.
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% (only 'ascending' is documented). The description adds that the column must be numeric and that results are highest or lowest, which helps map the column and ascending parameters. However, it does not clarify limit defaults or explicitly connect 'highest/lowest' to the ascending flag.
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 states a clear action (rank/top rows) on a specific resource (Handovra dataset) and explains the 'highest or lowest' semantic with an example question. It does not explicitly differentiate from sibling tools like dataset_stats, but the core purpose is unambiguous.
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?
No guidance is given on when to use this tool versus alternatives. The phrase 'which is the most/least X' implies a use case but never mentions when dataset_stats or dataset_search would be more appropriate.
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 are clearly differentiated by operation: schema, provenance, exact match, substring search, multi-value comparison, stats, and ranking. The only minor overlaps are dataset_row vs dataset_compare for single-value lookups and the shared metadata banner in dataset_columns vs the full provenance record.
All tools share a consistent lowercase dataset_ prefix and snake_case style, making the family immediately recognizable. The suffixes mix nouns (columns, provenance, row, stats) with verbs (compare, search), so it is not a strict verb_noun pattern, but the deviation is minor and readable.
Seven tools is well within the ideal range for a read-only dataset exploration server. Each tool covers a distinct query need without redundancy, and none feels like filler.
The set covers the core data-exploration lifecycle: schema discovery, provenance, exact lookup, text search, multi-value comparison, numeric summaries, and top/bottom ranking. It lacks direct numeric range filtering or group-by, but agents can often approximate these with top, stats, or search, so the gaps are minor rather than blocking.