dataset_topThe highest (or lowest) rows of the Eposvo 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 behavioral burden. It discloses the core behavior: returning highest/lowest rows by numeric column and supporting ascending order. It does not mention default limits, tie handling, null values, or the shape of the returned rows, leaving some behavior undocumented.
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 tight sentence that front-loads the core operation and adds a helpful natural-language use case. Every element earns its place, and there is no redundant padding.
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 three-parameter tool, the description plus schema is mostly workable, but important call-time facts are missing: the default limit when limit is omitted, the output format, and how this tool relates to sibling row/stat/search tools. The low schema coverage and absence of an output schema make these gaps noticeable.
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 useful meaning by saying the column must be numeric, but it adds nothing about the limit parameter or its default, and the ascending behavior is already stated in the schema. The compensation is partial.
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 of the Eposvo dataset by a numeric column and returns the highest or lowest ones, and the title reinforces the verb-resource pair. It does not explicitly call out sibling tools or contrast with dataset_stats/dataset_row, so it stops short of full differentiation.
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 this is for top/bottom-N natural language queries, giving an implicit usage cue. There is no explicit guidance about when to prefer dataset_top over dataset_stats, dataset_search, or dataset_row, and no exclusions are stated.
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, exact lookup, substring search, list comparison, summary stats, and top/bottom rows. There is mild overlap between dataset_compare and dataset_row since both return exact matches, but their use cases are described distinctly enough.
All tool names follow a consistent dataset_<operation> pattern with lowercase snake_case. While the suffixes mix nouns and verbs, the pattern is uniform and predictable across the entire set.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct common query pattern without unnecessary bloat or duplication.
The tools provide complete read-only coverage for exploring and reporting on a dataset: schema discovery, provenance, exact lookup, substring search, multi-value comparisons, numeric summaries, and top/bottom ranking. No major query pattern needed for this domain is missing.