dataset_topThe highest (or lowest) rows of the ReceivableLedger 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 behavior disclosure. It states the sorting direction (highest or lowest) but fails to mention the default limit, tie-handling, the output row shape, or whether this is a read-only operation.
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 is concise, though its brevity leaves out important behavioral details that would be expected for a tool with no annotations and no output schema.
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 the lack of annotations and output schema, the description should clarify defaults (e.g., default limit) and result format. It omits what the returned rows look like and does not explain the limit parameter's behavior, leaving an agent without enough information to guarantee a correct call.
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% (only 'ascending' is documented). The description adds some meaning to 'column' by specifying it must be numeric, but it does not explain the 'limit' parameter or its default behavior, so it does not compensate for the low schema coverage.
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 the task: 'Rank rows by a numeric column' and return 'the highest (or lowest) rows' of the ReceivableLedger dataset. The phrase 'which is the most/least X' makes the use case concrete and distinguishes it from siblings such as dataset_row (single row) and dataset_stats (aggregate statistics).
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 a usage scenario ('which is the most/least X') but does not explicitly state when to use this tool over siblings or what conditions would make an alternative preferable. There are no exclusions or alternative tool mentions.
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.
Most tools have clearly distinct purposes: schema, provenance, search, stats, and top are easy to tell apart. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by matching a column value, with only the number of allowed values clearly differing.
All seven tools share the same dataset_ prefix and consistent snake_case formatting, making the family immediately recognizable. While some suffixes are nouns and some are verbs, the overall convention is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct common operation without adding redundant or overwhelming surface area.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, fuzzy search, numeric statistics, and top/bottom ranking. Missing features like distinct-value listing or numeric-range filtering are minor gaps given the apparent Q&A-oriented purpose.