dataset_topThe highest (or lowest) rows of the Eobify 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 full burden of behavioral disclosure. It mentions highest/lowest ordering but does not disclose read-only nature, default limit behavior, tie handling, or what the returned rows look like. This is minimal behavioral context for a query tool with no annotation support.
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 concise sentence and is easy to scan. It front-loads the core operation and avoids filler, though the em-dash aside is slightly stylistic rather than informative.
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 is minimally viable: it identifies the dataset, the operation, and the key column type requirement. However, with no annotations and no output schema, it would be stronger if it mentioned return behavior, limit defaults, or tie-breaking rules.
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 the 'ascending' parameter documented. The description adds useful meaning by specifying that 'column' must be numeric and clarifying highest/lowest semantics, but it adds nothing about the 'limit' parameter beyond its name and schema constraints.
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 the tool ranks rows by a numeric column and answers 'which is the most/least X', which makes the operation easy to understand. It does not explicitly name or distinguish itself from sibling tools, but the top-N ranking intent is distinct from row lookup, search, stats, and compare tools.
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' implies when to use the tool: when ranking rows by a numeric value. However, it provides no explicit guidance about when not to use it or which sibling tool might be more appropriate for related tasks.
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 defined purpose, but dataset_row and dataset_compare overlap conceptually since both retrieve rows by column value, just with different cardinality and ordering. The other tools are clearly separated between schema, provenance, search, statistics, and top/bottom ranking.
All tool names consistently use the dataset_ prefix followed by a concise operation name in snake_case. The pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is a well-scoped set for a single-dataset server. Each tool covers a distinct common query type, and none feel redundant or unnecessary.
The tool surface covers the main dataset exploration needs: schema, provenance, exact lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no direct count of filtered rows or grouped aggregation, but agents can work around these with existing tools.