dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the TimeCardBook dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
Without annotations, the description carries full transparency burden. It enumerates exactly what information will be returned (columns, numeric flags, row count, provenance banner), giving a clear expectation of the tool's output and side-effect-free nature. It does not mention any destructive or state-changing behavior, which aligns with a read-only schema inspection.
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, compact sentence that contains all essential information without fluff. It is well-structured and immediately readable.
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 tool with no parameters and no output schema, the description fully covers what the agent needs to know: what data it returns and when to call it. The instruction to call first adds context that helps orchestrate with other dataset tools. No critical information is missing.
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 tool has zero parameters and the schema coverage is 100% (i.e., nothing to document). The description adds no parameter-specific semantics because there are none. This is the baseline score for a no-parameter tool.
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 returns columns, numeric flags, row count, and provenance banner for a specific dataset, and explicitly instructs to call it first to learn the schema. This unambiguously separates it from sibling tools 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?
It provides a direct usage directive: 'Call this first to learn the schema.' This tells the agent when to invoke it, though it does not explicitly contrast with alternatives. Still, the guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool addresses a distinct query pattern: schema discovery, provenance, exact-match lookup, substring search, ordered multi-value comparison, column statistics, and top/bottom row ranking. The only close pair is dataset_row and dataset_compare, but their descriptions clearly separate exact single-value equality from ordered value-list comparison.
All seven tools use the same dataset_ prefix and snake_case convention, producing a predictable and scannable set. Although suffixes mix nouns (columns, stats) and verbs (compare, search), the consistent prefix and clear semantic labels make naming highly regular.
Seven tools is well-scoped for read-only interrogation of a single dataset, covering metadata, lookup, search, comparison, statistics, and extreme values without redundancy. The count is comfortably in the ideal range for this purpose.
The surface covers the main dataset operations: schema, provenance, exact and fuzzy retrieval, comparisons, aggregates, and ranking. A minor gap is the lack of a way to retrieve all rows or list unique categorical values, but most realistic questions can be answered with the provided patterns.