dataset_topThe highest (or lowest) rows of the Duesvo 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 disclosing behavior. It does state the core behavior: sorting/filtering rows by a numeric column in ascending or descending order. But it does not disclose tie-handling, nulls, invalid column types, whether full rows are returned, or any default limit behavior.
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 compact sentence with no filler, and the 'most/least X' phrase adds interpretive value. It could be slightly clearer, but it earns a good score for efficiency and front-loading the core purpose.
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 no annotations, so the description needs to compensate. It leaves important operational details unspecified: the default limit, the shape of returned rows, sorting behavior for ties or non-numeric data, and how the result relates to the rest of the Duesvo dataset. This is enough to make a call, but not enough for confident invocation without additional inference.
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%, with only the 'ascending' parameter described. The description adds useful meaning for 'column' by saying it must be numeric, and it clarifies ascending/descending intent via 'highest (or lowest)'. However, it adds no semantics for 'limit', including what the default is or how it affects results.
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 by a numeric column and returns the highest or lowest rows, using the phrasing 'which is the most/least X' to make the intent understandable. It distinguishes itself from generic row retrieval or statistics tools, though it does not explicitly contrast with any sibling.
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 intended use is implied through 'highest (or lowest)' and 'most/least X', so an agent can infer when a ranking question is being asked. However, there is no explicit guidance about when to choose dataset_top over siblings such as dataset_stats, dataset_row, or dataset_search, and no exclusions or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools have clearly distinct purposes: schema, provenance, exact match, substring search, multi-value comparison, aggregate stats, and top/bottom ranking. The minor overlap between dataset_row, dataset_search, and dataset_compare could cause occasional misselection, but each description states its exact matching behavior.
All tools share the dataset_ prefix, which creates a strong family resemblance. However, the suffix is sometimes a noun (columns, provenance, row) and sometimes a verb (compare, search, stats, top), so the pattern is not fully uniform.
Seven tools is a well-scoped set for querying a single dataset. Each tool addresses a distinct common question type without redundancy or bloat.
The set covers schema discovery, provenance, exact lookups, fuzzy search, controlled comparison, numeric aggregates, and sorted extremes. A general arbitrary filter or grouped analysis is missing, but the provided tools handle the most likely dataset questions.