dataset_topThe highest (or lowest) rows of the Dispatchzo 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 must disclose behavior on its own. It states that it returns the highest or lowest rows by a numeric column, which covers the core behavior. However, it does not mention default ordering, the meaning of limit, or potential limitations such as ties or non-numeric column handling.
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 a clarifying use-case phrase. It is front-loaded with the operation and resource, contains no filler, and every part adds meaning.
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 simple ranking tool, the description covers purpose and the numeric column requirement, and the schema covers limit constraints and ascending default. Still, there is no mention of output shape, no usage guidance versus siblings, and no handling of edge cases, leaving the overall context only minimally complete.
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 should compensate. It adds the 'numeric column' constraint and hints at ascending/descending via 'highest or lowest,' but it does not explain the limit parameter at all, nor does it clarify how the three parameters interact. The schema alone leaves too much implicit.
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 states the operation precisely: 'Rank rows by a numeric column.' The description reinforces this with 'The highest (or lowest) rows ... by a numeric column' and gives a concrete use case ('which is the most/least X'). This clearly distinguishes it from siblings 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 usage for finding extremes or top/bottom rows, but it never explicitly says when to prefer this tool over alternatives, nor does it mention sibling tools or exclusion conditions. The use-case phrasing gives context, but there is no direct guidance.
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 query need: schema, provenance, exact row lookup, substring search, multi-value ordered comparison, numeric stats, and top/bottom ranking. There is no meaningful overlap that would make an agent uncertain which tool to call.
All tools follow a consistent `dataset_` prefix with concise snake_case names that clearly reflect their function. The naming convention is uniform and predictable across the entire set.
Seven tools is a well-scoped set for a dataset-querying server. Each tool covers a distinct operation and none are redundant or superfluous.
The toolset covers schema discovery, provenance, exact and substring search, comparison, statistics, and ranking—strong coverage for a dataset Q&A server. Minor gaps exist such as no direct way to fetch all rows, list distinct values, or filter on multiple columns simultaneously, but these can usually be worked around with the existing tools.