dataset_topThe highest (or lowest) rows of the Keysvo 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?
There are no annotations to indicate read-only behavior or side effects. The description gives some behavioral insight (ordering by highest/lowest, numeric column) but does not mention defaults for limit or ascending, nor whether it returns full rows or just values. Since no annotations are present, the description carries the burden but only partially fulfills it.
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 that directly conveys the tool's purpose. It avoids unnecessary detail and is well-structured for quick comprehension.
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 simple ranking tool, the description covers the core logic but omits important context such as the shape of the returned data (full rows vs. specific fields) and the default value for limit. Without an output schema, this missing information could lead to incorrect assumptions about the tool's 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?
The schema provides a description only for 'ascending', leaving 'column' and 'limit' without explanation. The description adds that the column must be numeric and clarifies the meaning of ascending (most/least), but does not explain the limit parameter or its default behavior. Overall schema coverage is low, and the description partially compensates.
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 returns the highest or lowest rows by a numeric column, which is a specific verb and resource. It distinguishes itself from sibling tools like dataset_search (which likely filters) and dataset_stats (which aggregates) by focusing on ranking. The title reinforces this purpose.
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 usage for answering 'which is the most/least X' but does not explicitly state when to prefer this over alternatives such as dataset_search or dataset_stats. No direct comparison or exclusion is provided, leaving the agent to infer the appropriate context.
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 targets a distinct query mode: schema, metadata, exact match, substring search, multi-value comparison, statistics, and top/bottom ordering. There is minor overlap between dataset_compare and dataset_row since both filter on column values, but their descriptions clearly separate multi-value ordered lookups from single exact matches.
All tools share the consistent dataset_ prefix and snake_case format, which makes the family easy to recognize. However, the suffixes mix nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the pattern is consistent in prefix but not perfectly uniform in part of speech.
Seven tools is well-scoped for a dataset query server. Each tool covers a distinct useful operation without redundancy or bloat, and the count feels appropriate for the stated purpose.
The toolset covers the full read-only dataset lifecycle: schema discovery, provenance, exact lookup, fuzzy search, value comparison, numeric statistics, and top/bottom ranking. There are no obvious dead ends for common dataset questions, and no missing operations seem necessary for the domain.