dataset_topThe highest (or lowest) rows of the Consentvia 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 burden of behavioral disclosure. It states that the tool selects highest or lowest rows and that ordering is based on a numeric column, which is the core behavior. It does not detail edge cases such as null values, non-numeric columns, tie handling, or whether full rows are returned, but the primary behavior is transparent.
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, well-structured sentence that front-loads the operation and resource. The quoted 'which is the most/least X' is a helpful semantic gloss rather than filler, and there is no redundant phrasing.
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 description plus schema covers the core contract: numeric column, ordering direction, and limit bounds. However, with no output schema and no annotations, the agent is left without explicit guidance on output shape, default limit, or how this tool should be chosen over dataset_stats or dataset_search. These are meaningful gaps for a tool that returns ranked rows.
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%, so the description must compensate. It adds meaning for 'column' by specifying it must be numeric and clarifies the intent of 'ascending' through 'highest or lowest.' However, the 'limit' parameter is not explained beyond its schema constraints, and there is no mention of default limit behavior when omitted.
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 names the operation: return the highest or lowest rows of the Consentvia dataset, ordered by a numeric column. It also translates the operation into a plain-language question ('which is the most/least X'). However, it does not explicitly distinguish itself from sibling tools like dataset_search or dataset_stats, so differentiation relies on the operation name rather than direct comparison.
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 gives clear usage context: use this when the query is about ranking rows by a numeric value, e.g., 'which is the most/least X.' It does not mention alternatives or when not to use the tool, but the context is specific enough that an agent can infer the intended use case.
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 are clearly distinct: schema, provenance, search, stats, and top each target a different operation. The only ambiguity is between dataset_row and dataset_compare, which both filter rows by column values, though compare explicitly handles multiple values and ordering.
All tools share a consistent dataset_ prefix and use short, descriptive nouns or verbs that align with their function. The naming pattern is uniform and predictable, making it easy to infer what each tool does.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct useful query pattern without unnecessary redundancy or bloat.
The server covers the core dataset exploration lifecycle: schema inspection, provenance, exact lookups, multi-value comparisons, text search, numeric statistics, and top/bottom sorting. Minor gaps exist such as pagination or arbitrary multi-condition filtering, but most common questions about the dataset can be answered.