dataset_topThe highest (or lowest) rows of the Endpointo 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 full burden of behavioral disclosure. It mentions highest/lowest rows and numeric column but does not state the default ordering (though the schema does), limit behavior, handling of non-numeric columns, ties, or what the response includes (e.g., full rows vs. column values). These gaps leave the agent uncertain about side effects and edge cases.
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, front-loaded sentence with no filler. It efficiently communicates the core action and purpose, making it easy for an agent to scan and understand quickly.
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?
Given three parameters, no output schema, and no annotations, the description is insufficiently complete. It fails to describe the return format, pagination or limit behavior, error conditions (e.g., non-numeric column), or how to interpret results. The agent cannot fully anticipate the tool's behavior from the description alone.
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?
The description adds meaning for 'column' (must be numeric) and for 'ascending' (highest vs. lowest), which complements the schema. However, it does not elaborate on 'limit' semantics (e.g., default value, max) beyond the schema's min/max. With schema coverage at only 33%, the description partially compensates but does not fully clarify all parameters.
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's function: it ranks rows of the Endpointo dataset by a numeric column and returns the highest or lowest rows. The phrasing "which is the most/least X" makes the purpose specific and distinguishable from sibling tools like dataset_stats (which likely aggregates) and dataset_search (which likely filters).
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?
There is no guidance on when to use this tool versus alternatives. The description implies a ranking use case but does not mention when to prefer it over dataset_row, dataset_search, or dataset_stats. No exclusions or alternative names are given, leaving the agent to infer applicability.
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 has a clearly distinct purpose: schema, exact match, search, comparison, stats, top values, and provenance. No two tools overlap in functionality; an agent can easily select the right tool based on the query type.
All tool names follow the consistent 'dataset_' prefix followed by a descriptive noun (columns, compare, provenance, row, search, stats, top). The naming pattern is uniform and predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a specific aspect of querying and understanding the dataset without unnecessary redundancy or bloat.
The surface covers schema, metadata, exact lookup, substring search, comparison, statistics, and ordering. It lacks features like distinct value enumeration or grouping, but for a read-only dataset exploration tool, the coverage is strong with only minor potential gaps.