dataset_topThe highest (or lowest) rows of the Dividvo 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 must carry the behavioral burden. It does disclose the core behavior: returning top/bottom rows by a numeric column, with an ascending option implied by 'highest (or lowest)'. However, it does not mention default limit behavior, what happens when limit is omitted, tie-breaking, or handling of non-numeric data, which are relevant for a ranking tool.
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 efficiently communicates the primary function and the ranking question it answers, though the 'Dividvo dataset' phrasing and the em-dash aside are slightly cryptic.
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 is thin. It does not explain the limit behavior, nor does it provide guidance on choosing between dataset_top and closely related siblings like dataset_search, dataset_stats, or dataset_row. The core idea is present, but an agent is left without enough context to invoke it optimally in all cases.
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 column and limit lacking descriptions. The description adds meaning for column (numeric ranking column, 'most/least X') and partially for ascending, but it does not explain the limit parameter at all—its default, whether it is required, or what a missing limit yields. This leaves a significant parameter-semantics gap.
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 clearly state that this tool returns the highest or lowest rows of a dataset ordered by a numeric column, which is a specific verb+resource combination. It conveys the core ranking purpose and even gives an example question ('which is the most/least X'), though it does not explicitly distinguish itself from sibling tools like dataset_row or dataset_search.
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 implies the tool is for finding extreme values ('the highest or lowest rows' and 'which is the most/least X'), but it does not explicitly say when to prefer it over siblings such as dataset_search, dataset_stats, or dataset_row. There are no exclusions or alternative-suggestion statements, leaving the decision to inference.
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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Each tool has a clearly different purpose: schema, provenance, exact match, search, stats, ranking, and comparison. dataset_row and dataset_compare overlap for single-value equality, but the descriptions make the ordered multi-value use case clear.
All tools follow the same dataset_ prefix with a descriptive noun or verb, forming a highly predictable naming pattern. There is no mixing of conventions or vague generic names.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct query mode without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact filtering, substring search, numeric statistics, top/bottom ranking, and comparisons. Minor gaps exist such as pagination or listing all rows, but agents can accomplish most dataset tasks with these tools.