dataset_topThe highest (or lowest) rows of the Cmmsly 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 behavior disclosure. It only states that the highest or lowest rows are returned, but does not mention output shape, tie handling, missing values, non-numeric column behavior, or that it is a read-only operation. With no annotation safety net, this is a significant gap.
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 with no filler. The core idea is front-loaded ('highest or lowest rows'), and the quoted 'which is the most/least X' is a compact, memorable framing that earns its place.
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 3 parameters, no annotations, and no output schema, the description is too thin. It does not clarify what the result looks like, how many rows are returned by default, or how the 'limit' parameter interacts with the ranking. An agent calling this tool correctly would need to infer too much.
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 adds valuable meaning to the required 'column' parameter by specifying it must be numeric. However, it does not explain the 'limit' parameter's semantics at all, leaving a key aspect of the tool's behavior undocumented.
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 title 'Rank rows by a numeric column' plus the description 'The highest (or lowest) rows of the Cmmsly dataset by a numeric column' clearly state a specific verb, resource, and scope. The phrase 'which is the most/least X' gives an intuitive framing that distinguishes it from sibling tools like dataset_search or dataset_stats.
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 when to use the tool: when you need the most/least extreme rows by a numeric column. However, it does not explicitly mention any alternatives or exclusion conditions, so an agent must infer the intended context from the purpose statement alone.
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.
The seven tools are mostly distinct: schema, provenance, exact-match row lookup, substring search, value-list comparison, numeric stats, and top-N ranking each serve a clear purpose. dataset_row and dataset_compare both filter rows by column values, and dataset_stats/dataset_top both operate on numeric columns, so there is minor potential for confusion, but the descriptions disambiguate them adequately.
All tools share a consistent dataset_ prefix and use lowercase snake_case throughout. The second segment is a concise operation or concept (columns, compare, provenance, row, search, stats, top), providing a predictable naming pattern.
Seven tools is well-scoped for a single-dataset exploration server. Each tool addresses a distinct query type without redundancy or bloat.
The surface covers the full range of common dataset questions: schema discovery, provenance/citation, exact and fuzzy row retrieval, value comparison, numeric statistics, and ranking. No significant dead ends or missing operations are evident for the stated purpose.