dataset_topThe highest (or lowest) rows of the ProbeLedger 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 mentions sorting direction (highest/lowest) but does not disclose behavior on ties, missing values, limit enforcement, determinism, or any side effects (presumably read-only but unstated). The description is too thin for a mutation-free tool to fully inform the agent.
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 wasted words. It conveys the core purpose and a practical interpretation in an efficient manner, making it easy for an agent to parse 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?
This is a simple tool, but without an output schema the description should indicate what the tool returns (e.g., a list of rows). It does not mention the return format, error conditions, or how the 'limit' interacts with sorting. Several important operational details are missing for an agent to call it confidently.
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' has a description). The description adds the detail that the column must be numeric, which is helpful, but it does not explain the 'limit' parameter or its maximum value, nor clarify the default behavior beyond what the schema already states. It fails to compensate for the low schema coverage.
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 states a specific verb ('rank rows') with a resource ('ProbeLedger dataset') and a clear scope (by a numeric column). It also includes a natural language interpretation ('which is the most/least X'), making the tool's intent immediately clear and distinct from sibling tools that handle columns, rows, search, or 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 a use case (finding extreme rows by a numeric metric) but does not explicitly state when to prefer this tool over alternatives like dataset_search or dataset_stats, nor does it mention any exclusions. It provides enough context for a basic understanding but lacks direct routing 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.
Most tools are distinct (schema, provenance, stats, top, compare), but dataset_row, dataset_search, and dataset_compare all return rows with subtly different matching semantics, which could confuse an agent. The descriptions help, but the boundaries between exact match, substring search, and ordered value comparison are not immediately obvious.
All tools share the consistent 'dataset_' prefix and use lowercase snake_case, which creates a clear family identity. However, the suffixes mix nouns (columns, row, stats, provenance) with verbs (compare, search, top), so the pattern is not perfectly uniform.
Seven tools is a well-scoped number for a single-focused dataset exploration server. Each tool addresses a distinct query need without redundancy or bloat.
The toolkit covers schema discovery, statistics, filtering, ranking, comparisons, and provenance, which covers most dataset exploration workflows. A minor gap is the lack of a way to retrieve arbitrary rows or the full dataset without a matching condition.