dataset_topThe highest (or lowest) rows of the DailyLogDesk 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?
No annotations are provided, so the description carries the behavioral disclosure burden. It does state the core behavior (highest or lowest rows by a numeric column), but it does not describe limit/default behavior, tie handling, non-numeric handling, or the returned row shape. Without an output schema, this is a significant gap.
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 one front-loaded sentence containing the resource, criterion, direction, and a usage phrase. There is no filler or redundant elaboration.
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 should clarify result count, limit semantics, and return value shape. It does not, so although the purpose is clear, an agent cannot confidently predict the outcome for all valid calls.
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 'ascending' described. The description adds the important fact that the column must be numeric and connects ascending to lowest-first ordering, but it does not explain 'limit' or its default behavior, leaving a key optional parameter under-specified.
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 identify the operation: rank rows of the dataset by a numeric column and return the highest or lowest rows. It is distinguishable from siblings like dataset_stats or dataset_row, though it does not explicitly name or exclude them.
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' supplies the intended query context, implying use when the user wants extreme values. However, it does not state when to prefer dataset_stats, dataset_search, or dataset_row, nor does it give explicit exclusions or conditions not to use this tool.
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 clearly stated query mode, but several return rows from the same dataset with overlapping semantics (exact match, substring search, multi-value compare, top-N). An agent could sometimes confuse dataset_row and dataset_search, though the descriptions provide enough detail to disambiguate.
All tools follow a consistent dataset_ prefix pattern with short, descriptive operation names. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct access pattern without unnecessary duplication or bloat.
The toolset covers schema discovery, provenance attribution, exact row lookup, substring search, value comparison, numeric statistics, and top/bottom ranking. For a read-only dataset querying server, this is a complete surface with no obvious dead ends.