dataset_topThe highest (or lowest) rows of the Exitvo 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 states the core highest/lowest behavior and numeric-column requirement, but it does not mention default ordering, how limit applies, behavior with non-numeric values or ties, or whether the operation is read-only. This leaves important behavioral traits undisclosed.
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, front-loaded sentence with no filler. It states the key idea compactly and the explanatory phrase adds value without excessive length.
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 annotations, no output schema, and only partial parameter documentation, the definition is too thin for fully confident invocation. It explains the purpose but leaves out how limit and ascending interact, what the returned rows look like, and 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%, so the description must compensate, but it only adds meaning for the column parameter ('numeric column'). It does not explain the 'limit' parameter or connect the 'highest/lowest' wording to the 'ascending' parameter default.
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 conveys that dataset_top returns the highest or lowest rows by a numeric column, and the parenthetical 'which is the most/least X' clarifies the intended ranking use case. It is specific enough to be distinguished from siblings like dataset_search or dataset_stats, though it lacks an explicit verb beyond the title.
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 when this tool is appropriate, but the description does not explicitly contrast it with sibling tools such as dataset_search or dataset_stats, nor does it mention any prerequisites or exclusions. Usage guidance is present but not fully explicit.
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 role: schema, provenance, exact row lookup, substring search, multi-value comparison, numeric stats, and top/bottom rows. The only minor overlap is between dataset_row and dataset_compare, but their descriptions clearly separate single-exact-match from multiple-value in-order filtering.
All tools share the consistent 'dataset_' prefix with short, readable suffixes. Most suffixes are nouns (columns, row, stats, top), while 'compare' and 'search' read as verbs, a small grammatical deviation from an otherwise uniform pattern.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a meaningful query operation—metadata, lookup, search, aggregation, sorting—without redundancy or bloat.
The surface covers core dataset workflows: understanding schema, citing provenance, finding rows by exact match or substring, comparing values, computing statistics, and identifying extremes. A minor gap is the lack of distinct-value listing or pagination, but the main use cases are well supported.