dataset_topThe highest (or lowest) rows of the PunchListWorks 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 must disclose behavior itself; it does state highest/lowest ordering and numeric-column constraint. It omits details like default row limit, behavior when limit is omitted, tie handling, or returned fields, leaving notable behavioral 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?
One tightly written sentence that front-loads the operation, names the resource, and gives a memorable use-case phrase. There is no filler or redundant restatement of the 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?
For a tool with no annotations and no output schema, the description should clarify what calling it returns and how optional parameters like limit behave. The description leaves the default limit and output shape unspecified, so an agent cannot reliably predict the tool's behavior beyond the basic ranking concept.
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%, and while the description adds the 'numeric column' constraint and clarifies highest/lowest semantics, it adds no explanation for the limit parameter. The limit's optional behavior and interaction with ranking are left entirely to the schema's bare minimum/maximum bounds.
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 identifies a ranking operation on rows by a numeric column, with a concrete use case ('which is the most/least X'). It is specific but does not distinguish itself from sibling tools such as dataset_stats or dataset_search.
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 'which is the most/least X' phrasing provides a clear context for when to use this tool. However, it never mentions alternatives or when not to use it, so it stops short of full routing guidance.
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 are clearly distinct: columns/provenance/stats/top serve different purposes, while row, search, and compare all retrieve rows but with different matching semantics. dataset_compare and dataset_row could be confused at first glance, but the descriptions clarify exact vs. contains vs. ordered multi-value comparisons.
All seven tools share the consistent dataset_ prefix followed by a short, meaningful descriptor (columns, compare, provenance, row, search, stats, top). Even though some suffixes are nouns and some verbs, the pattern is highly predictable and uniform.
Seven tools is a well-scoped size for a read-only dataset exploration server. Each tool covers a distinct useful operation without bloat or significant redundancy.
The surface covers schema discovery, provenance, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking, which suits a read-only dataset. Missing operations like group-by or distinct-value summaries are minor and not critical for the apparent purpose.