dataset_topThe highest (or lowest) rows of the Sittingly 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 provided, the description carries the full burden of behavioral disclosure. It mentions 'highest (or lowest)' but does not explain sorting behavior, tie handling, limit semantics, or what happens with non-numeric columns. The description adds minimal value beyond the schema's ascending parameter description.
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, short sentence with no waste. It front-loads the core purpose. However, it is slightly vague with the quote, but overall it is appropriately concise.
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 ranking tool with no output schema, no annotations, and incomplete parameter documentation, the description is insufficient. It does not clarify limit behavior, ascending default, or output format. The agent would need to guess or rely on the schema, which is also incomplete.
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 coverage is only 33% (only 'ascending' has a description). The description helps clarify that 'column' must be numeric, but it does not explain the 'limit' parameter at all. Given low schema coverage, the description should compensate, but it only partially does so for one parameter.
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 states a clear purpose: ranking rows by a numeric column to find highest/lowest values. It is specific and distinct from sibling tools like dataset_search or dataset_stats, though it doesn't explicitly name alternatives. The title reinforces this.
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 gives no guidance on when to use this tool versus siblings. It doesn't mention alternative tools or conditions that would make another choice more appropriate. The agent is left to infer usage 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.
Most tools have clearly distinct purposes: schema discovery, provenance, exact lookup, substring search, stats, and top/bottom ranking are easy to tell apart. However, dataset_row and dataset_compare overlap in that both do exact column-value matching, with compare merely extending row to multiple values, which could cause some selection uncertainty.
All tools share the clean dataset_ prefix and use lowercase snake_case, making the set feel cohesive. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped size for a dataset-querying server. Each tool covers a distinct query need without redundancy or bloat, and the count is comfortably within the ideal range.
The server covers the full read-only lifecycle of working with a dataset: schema discovery, provenance attribution, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking. There are no obvious dead ends for the stated purpose.