dataset_topThe highest (or lowest) rows of the LPreportly 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/lowest rows and numeric columns, but it doesn't state behavior around limit defaults, empty results, ties, non-numeric values, or whether the full row or only the selected column is returned. This leaves significant behavioral 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 mostly front-loaded, but the phrasing is awkward ('The highest (or lowest) rows of the LPreportly dataset...') and includes the confusing 'LPreportly' typo. The trailing quote adds a use-case but could have been integrated more cleanly. It is concise but not well-structured.
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?
Given no output schema, no annotations, and only 33% parameter coverage, the description is too thin. It doesn't clarify the output shape, the meaning of 'limit', or edge-case behavior. An agent would need to infer or test to use this tool reliably, especially with six sibling tools that could overlap in function.
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 low (33%): only 'ascending' has a description. The description ties 'highest/lowest' to ascending but says nothing about 'column' semantics beyond 'numeric' or about 'limit', including its default and upper bound. This is insufficient compensation for the undocumented schema fields.
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 provides a use-case ('which is the most/least X'), so an agent can grasp the core function. It doesn't explicitly differentiate from sibling tools like dataset_stats or dataset_row, and the odd placeholder 'LPreportly' adds minor confusion.
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?
There is no guidance on when to use this tool versus alternatives such as dataset_stats or dataset_search. The description only says what it does, not in what situations it should be preferred, nor any exclusions like 'use dataset_row for exact matches'.
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, provenance, exact row lookup, text search, multi-value comparison, stats, and top-N. dataset_row and dataset_compare could be confused since both filter on column values, but compare explicitly handles multiple given values and ordering.
All tools share a consistent dataset_ prefix and use lowercase snake_case, making the pattern predictable. However, the second part mixes noun-style names (columns, provenance, row) with verb/action-style names (compare, search, stats, top), so it is not a uniform verb_noun convention.
Seven tools is a well-scoped size for a single-dataset read-only server. Each tool provides a distinct mode of access or summary without unnecessary redundancy or bloat.
The set covers schema discovery, provenance, exact-value lookup, substring search, multi-value comparison, numeric statistics, and top/bottom rows. Minor gaps exist, such as no pagination or distinct-value listing, but most dataset exploration questions can be answered with the available tools.