dataset_topThe highest (or lowest) rows of the Scopedly 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 full responsibility for disclosing behavior. It conveys a read-only ranking operation but does not mention side effects, error behavior, or return format. It is not misleading, but it is minimal.
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 with an illustrative example. It front-loads the key behavior ('highest or lowest rows') and avoids unnecessary wording.
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?
The definition is adequate for a simple query tool but lacks details about the output structure (e.g., whether full rows are returned) and edge cases like ties or null values. Given no output schema, a bit more context would be helpful.
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?
Only one of three parameters (ascending) has an inline description; column and limit are undocumented. Schema coverage is low, and the description does not clarify that column must reference a numeric field or what limit defaults to. Additional parameter guidance is needed.
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 returns the highest or lowest rows by a numeric column and provides a natural-language example ('which is the most/least X'). It is easily distinguished from siblings like dataset_row, dataset_search, and 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 use for ranking questions but does not explicitly state when to prefer this over alternatives such as dataset_row or dataset_stats. No explicit guidance is given about when not to use the tool.
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 targets a distinct dataset operation, though dataset_row, dataset_search, and dataset_compare all retrieve rows via different matching semantics. The descriptions clearly separate exact equality, substring containment, and multi-value ordering, so an agent can select correctly.
All tool names follow a consistent dataset_<noun> pattern with snake_case throughout. The naming makes the tool family immediately recognizable and predictable.
Seven tools is a well-scoped set for a dataset exploration server. Each tool covers a necessary operation—schema, provenance, lookup, search, comparison, stats, and top rows—without redundancy.
The set covers the core dataset exploration lifecycle well: schema discovery, provenance, exact and fuzzy retrieval, statistics, and ranking. A minor gap is the lack of a general sample/random row tool, but existing operations are sufficient for most dataset questions.