dataset_topThe highest (or lowest) rows of the Lanyardo 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 carries the full burden. It discloses the highest/lowest behavior and the role of the 'ascending' parameter, but does not mention edge cases (e.g., null values, tie-breaking, sorting stability), the return format, or any side effects. It adds modest behavioral context beyond the title but leaves significant 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 that front-loads the purpose and includes a clarifying example. There is no fluff or redundancy, making it appropriately sized and 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 and no annotations, the description should explain what the tool returns (e.g., full rows vs. just values), the default limit, and error conditions. None of these are addressed. For a ranking tool, an agent would benefit from knowing whether it returns the actual rows or only identifiers, and what happens with non-numeric columns. The description is too sparse for a complete understanding.
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% (ascending is described). The description adds value by clarifying that the column must be numeric, which is not in the schema. However, it does not explain the 'limit' parameter's default or behavior, and the 'ascending' parameter is already described in the schema. It partially compensates for low coverage but not completely.
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 states a specific action: 'Rank rows by a numeric column,' and clarifies it returns the highest or lowest rows via the 'ascending' parameter. The phrase 'which is the most/least X' makes the intent unmistakable and distinguishes it from siblings like dataset_stats (which computes aggregates) and dataset_search (which searches).
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 for ranking questions ('which is the most/least X') but does not explicitly mention when to prefer this over sibling tools or when not to use it. There are no exclusions or alternative references, so an agent must infer the right context.
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 distinct purpose: schema inspection, exact match lookup, substring search, comparison of multiple values, stats computation, top/bottom ranking, and provenance metadata. There is no ambiguity about when to use which tool.
All tools follow a uniform 'dataset_' prefix followed by a descriptive noun or verb (columns, compare, provenance, row, search, stats, top). The naming pattern is consistent and predictable.
Seven tools is well-scoped for a dataset querying server. Each tool covers a distinct operation without redundancy, and the count feels neither sparse nor bloated.
The surface covers schema discovery, data retrieval via exact match, substring search, multi-value comparison, numeric statistics, top/bottom ranking, and provenance. For a read-only dataset server, this is a complete set with no obvious gaps.