dataset_topThe highest (or lowest) rows of the Nofovo 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 mentions 'highest (or lowest)' but does not explain tie-breaking, return format (full rows or just values), default ordering, or behavior when the column is non-numeric. The description also does not mention that the ascending parameter controls order or that limit is capped at 50. These gaps leave the agent guessing about important execution details.
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 short sentence, which is concise and front-loaded with the core purpose. However, it is so terse that it sacrifices necessary detail. It is not verbose, but the brevity is more under-specification than disciplined conciseness.
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 three parameters, no output schema, and no annotations, the description is incomplete. It does not mention the return format, default ordering, or edge cases such as missing columns or empty results. An agent would need to infer too much to call the tool correctly, especially regarding limit and ordering behavior.
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 clarifies that column must be numeric but does not explain the limit parameter or its range, nor does it elaborate on column format or requiredness beyond the schema. Since coverage is low, the description should compensate, but it adds little for limit and nothing for column semantics beyond 'numeric'.
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 by a numeric column and returns the highest or lowest, with an illustrative question ('which is the most/least X'). The title confirms the verb 'Rank', and the resource (dataset rows) is explicit. It is distinct from sibling tools that handle columns, comparison, provenance, single rows, search, and statistics.
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 no guidance on when to use this tool versus siblings like dataset_search or dataset_stats. It offers an example question but does not specify when not to use it or mention alternatives. An agent cannot determine whether this or another tool is more appropriate for a given request without additional 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.
Most tools have clearly distinct purposes: schema, provenance, stats, top, search, exact-row, and compare all serve different question types. However, dataset_row and dataset_compare both filter by column values and could be confused for single-value lookups, and dataset_search adds a third overlapping retrieval path.
All tools share a consistent dataset_ prefix and use lowercase snake_case, making the set predictable. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the naming is mostly consistent but not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool addresses a distinct common question type, and none feel redundant or unnecessary.
The set covers schema discovery, provenance, exact lookups, fuzzy search, comparisons, summary statistics, and extreme values, which handles most dataset question-answering needs. Minor gaps exist around arbitrary inequality filters or combining conditions across multiple columns, but agents can typically work around these.