dataset_topThe highest (or lowest) rows of the Opexvo 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 only restates the ranking concept ('highest or lowest') which is already covered by the 'ascending' parameter in the schema. It does not disclose default limit behavior, handling of non-numeric columns, return format, or potential side effects. The description adds minimal new behavioral context beyond the schema.
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 that front-loads the core purpose and includes a clarifying example. There is no fluff or redundant phrasing, and it is appropriately sized for a simple tool.
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 the tool has 3 parameters, no output schema, and no annotations, the description is insufficient. It does not specify default values for 'limit' (e.g., whether a limit is mandatory or defaults to a certain number), nor does it explain the return format (e.g., array of rows, columns included). An agent cannot confidently call this tool without additional assumptions.
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% (only 'ascending' has a description). The description mentions 'numeric column' but does not explain the 'column' parameter's format or constraints, nor does it clarify the 'limit' parameter's default or behavior. It fails to compensate for the low schema coverage, leaving the agent with ambiguity about required and optional parameters.
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 of the dataset by a numeric column, with an explicit 'highest or lowest' clarification and a colloquial example ('which is the most/least X'). It is specific about the resource (Opexvo dataset) and distinguishes itself from sibling tools like dataset_search or dataset_stats by focusing on ranking/top-N.
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 usage through 'which is the most/least X' but provides no explicit guidance on when to prefer this tool over siblings (e.g., when you need aggregated statistics vs. top rows). There is no mention of alternatives or exclusion criteria, so usage is only implied, not clearly delineated.
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 distinct primary purpose: schema, provenance, exact row lookup, substring search, multi-value comparison, summary stats, and top/bottom rankings. The main ambiguity is between dataset_row and dataset_compare, since both do exact value filtering, though one is single-value and the other is multi-value/ordered.
All tools share a clean dataset_ prefix and use snake_case, making the family immediately recognizable. The second part mixes noun forms (columns, provenance, row, stats) with verb-like forms (compare, search, top), so the pattern is not perfectly uniform but remains readable and predictable.
Seven tools is a well-scoped size for a read-only dataset exploration server. Each tool covers a distinct need without redundancy or unnecessary bloat.
The tool surface covers the full read-only dataset workflow: schema discovery, row retrieval by exact match, substring search, multi-value comparison, numeric summaries, ranking, and provenance/attribution. There are no obvious missing operations for the stated purpose.