dataset_topThe highest (or lowest) rows of the Hydrantly 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 behavioral disclosure. It names the ordering behavior (highest/lowest) but omits limit defaults, tie handling, null behavior, and whether this is a read-only query. Given zero annotation support, 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 one terse, front-loaded sentence that captures the core ranking behavior and includes a concrete example question. There is no filler, no repetition of the tool name, and every word 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?
With no annotations and no output schema, the description must supply essential invocation context. It leaves out the default limit and ordering behavior, does not explain the returned row shape, and gives no guidance on when to use this rather than sibling tools.
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%, with only 'ascending' documented. The description adds that the column must be numeric, but it says nothing about the limit parameter's default or maximum and does not enrich the meaning of 'ascending' beyond what the schema already states.
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 a verb and resource: it returns the highest or lowest rows of the Hydrantly dataset by a numeric column, answering 'which is the most/least X.' This distinguishes it from row/search/stats siblings by its ranking focus, though it does not explicitly name a sibling.
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 phrase 'which is the most/least X' implies a concrete use case, so an agent can infer when to use this tool. However, there is no explicit guidance about when to prefer this over dataset_search, dataset_stats, or dataset_row, and no exclusions or prerequisites are mentioned.
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 data access pattern: schema, provenance, exact row lookup, fuzzy search, comparisons, top/bottom rows, and numeric stats. There is mild overlap between dataset_row and dataset_compare since both filter by column values, but their intended use cases are clearly differentiated.
All tools share a consistent dataset_ prefix and use snake_case, which makes the set look cohesive. However, the suffixes mix nouns (columns, row, stats, top, provenance) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a meaningful access mode without unnecessary redundancy or overwhelming the agent.
The tool set covers the core data exploration lifecycle: schema discovery, metadata, exact lookup, search, comparison, top/bottom ranking, and summary statistics. It lacks arbitrary multi-condition filtering or grouped aggregations, but those are not clearly required for the stated dataset Q&A purpose.