dataset_topThe highest (or lowest) rows of the Focusvo 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 behavioral burden. It discloses the core behavior: sorting rows by a numeric column and returning the highest or lowest set. It does not mention tie-breaking, null handling, non-numeric column errors, whether the operation is read-only, or what shape the results take, leaving some ambiguity.
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 short and front-loaded, with the core ranking behavior stated immediately. The illustrative 'which is the most/least X' adds practical query intuition without unnecessary fluff. It is slightly redundant with the title, but the extra nuance is useful.
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 three-parameter tool, the description plus schema covers the main invocation. Missing elements include explicit sibling-tool routing, behavior on invalid or non-numeric columns, and any statement about the returned row set. These gaps are moderate rather than severe.
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%, so the description must compensate. It adds meaning to 'column' by specifying it must be numeric and clarifies the 'ascending' behavior via highest/lowest. However, it does not explain the 'limit' parameter or the default ordering behavior beyond what the schema already provides.
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: ranking rows of a dataset by a numeric column and returning the highest or lowest values. The phrase 'which is the most/least X' gives a concrete query framing. It does not explicitly differentiate from sibling tools like dataset_row or dataset_stats, but the ranking intent is unmistakable.
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 use case: answer questions about most/least X in the dataset. However, it provides no explicit guidance about when to prefer dataset_top over related tools such as dataset_search or dataset_stats, and no when-not-to-use conditions are stated.
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 specific purpose: schema, provenance, exact lookup, multi-value comparison, search, stats, and top-N. dataset_compare and dataset_row overlap somewhat since both filter rows by column values, but the multi-value ordered behavior of dataset_compare and the exact single-value behavior of dataset_row keep them mostly distinct.
All tool names follow the same dataset_<noun> pattern, making the set predictable and easy to navigate. The naming clearly signals that every tool operates on the same Focusvo dataset.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct mode of querying or metadata retrieval without redundancy or unnecessary bloat.
The set covers schema discovery, provenance, exact lookup, search, comparison, statistics, and top-N queries, which are the core operations for exploring a dataset. Minor gaps exist such as distinct-value enumeration or grouped aggregation, but agents can work around them with existing tools.