dataset_topThe highest (or lowest) rows of the Extinvo 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 does convey the core behavior: ranking rows by a numeric column and returning either highest or lowest. However, it does not mention limit defaults, output shape, null handling, or error behavior, leaving notable gaps.
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, focused sentence with no filler, and it front-loads the key behavior and use case. It is concise and readable, though the title and description repeat the same basic idea.
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?
The tool is simple, but with no output schema and no annotations, the description should provide more detail about what the returned rows look like and how the optional limit behaves. The current description is adequate for a basic understanding but incomplete for confident invocation in edge cases.
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%, since only 'ascending' is described. The description adds important semantics by stating that 'column' must be numeric, which goes beyond the schema's 'string' type. However, it adds nothing about 'limit' and relies on the schema for 'ascending', so it only partially compensates for the low 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 description clearly states that the tool returns the highest or lowest rows of a dataset by a numeric column, with the 'which is the most/least X' framing making the intent unmistakable. This is specific enough to distinguish it from sibling tools like dataset_stats, dataset_search, and dataset_row.
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 provides clear context for when to use the tool: when answering 'which is the most/least X' questions about a numeric column. It does not explicitly name alternatives or state when not to use it, but the use case is clear enough for an agent to select it appropriately.
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.
The tools are mostly distinct: columns, provenance, stats, and top each have a single clear role, while dataset_row, dataset_search, and dataset_compare all return rows but differ by exact match, substring containment, and ordered value comparison. The descriptions explain these differences clearly, so misselection is unlikely but still possible.
Every tool follows the same dataset_<operation> pattern with a clear noun or verb suffix. The naming is predictable and the row-returning tools use distinct names (row, search, compare) that match their behavior.
Seven tools is well-scoped for exploring a single dataset. Each tool covers a meaningful operation and none feel redundant or superfluous.
The set provides schema, provenance, exact lookup, free-text search, compare, stats, and top/bottom ranking, which covers the main ways an agent would query this dataset. No obvious dead-end or missing core operation is apparent.