dataset_topThe highest (or lowest) rows of the TermsBird 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 present, so the description carries the full burden. It discloses the core behavior of ranking rows and hints at ascending/descending directions, but omits details such as what fields are returned, the default limit, and behavior for ties or missing numeric values.
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 that efficiently expresses the tool's purpose. The parenthetical 'which is the most/least X' is slightly redundant with 'highest or lowest' but adds an intuitive query framing without bloating the text.
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 3-parameter tool with no output schema, the description covers the core behavior. However, missing details about the default value for 'limit' and the exact structure of returned rows leave an agent with reasonable but incomplete guidance for a fully informed call.
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' is described). The description adds meaningful semantics for 'column' by specifying it must be numeric, and 'highest (or lowest)' reinforces the ascending option. However, 'limit' receives no semantic context from either the schema or the description.
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 specific operation: returning the highest or lowest rows of the TermsBird dataset by a numeric column, framed as 'which is the most/least X.' This goes beyond the title and distinguishes the tool from generic search or stats tools, though it does not explicitly differentiate from sibling tools by name.
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 'which is the most/least X' framing provides clear context for when to use this tool: when the user wants extreme rows by a numeric measure. It does not list alternatives or exclusions, but the intended use case is unambiguous.
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 discovery, filtering, provenance, exact lookup, substring search, statistics, and top/bottom sorting. No two tools overlap in function, and the descriptions reinforce the boundaries.
All tools follow the consistent dataset_<verb> pattern with no deviations. The naming convention is uniform and predictable.
7 tools is well-scoped for a dataset querying server. Each tool covers a necessary operation without redundancy or bloat.
The tool set covers schema, row retrieval, search, statistics, sorting, comparison, and provenance. The only minor gap is a straightforward 'get all rows' operation, but the existing tools can likely cover most workflows.