dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Sbarvo 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?
With no annotations provided, the description carries the full burden. It does disclose the return content (columns, numeric flags, row count, provenance banner), which is the key behavioral trait for a read-only introspection tool. However, it doesn't mention prerequisites (e.g., whether the dataset must be loaded) or any failure conditions, though these are minor for a zero-parameter read operation.
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?
Two sentences with zero waste: the first enumerates the return content, the second delivers the operational directive. Every word earns its place and the information is densely packed.
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 zero-parameter, read-only introspection tool with no output schema, the description is adequately complete. It names what is returned and how to sequence it. The only gaps — explicit sibling differentiation and failure modes — are minor given the simplicity of the tool.
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, so there is nothing to document beyond the schema. The description implicitly fixes the operating scope to 'the Sbarvo dataset,' which is the only contextual information needed. Baseline 4 applies for a parameterless 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 the provenance banner for the Sbarvo dataset — a specific resource with specific content. It distinguishes itself from the sibling set (provenance, stats, search, etc.) by being the schema-introspection tool. It's phrased as a noun phrase rather than an imperative verb+resource, but the meaning is unambiguous.
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?
'Call this first to learn the schema' is explicit, actionable guidance that tells the agent the correct sequencing before other operations. It doesn't explicitly name alternatives or state when NOT to use it, but the 'call first' directive effectively routes the agent here ahead of the data-oriented siblings.
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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Most tools have clearly distinct purposes, but dataset_row and dataset_compare can overlap when a single value is used, since both retrieve rows by column equality. dataset_search is distinct because it searches across all cells rather than a specific column.
All tools share the consistent dataset_ prefix, but the second part mixes nouns (columns, provenance, row, stats) and verbs (compare, search, top). The pattern is still predictable and readable, so it is only a minor deviation.
Seven tools is well-scoped for querying a single dataset: schema, provenance, exact lookup, substring search, ordered comparison, statistics, and ranking each earn their place. No tool feels redundant or excessive.
The tool surface fully covers read-only exploration of the Sbarvo dataset: learning the schema, checking provenance, retrieving rows by exact match or substring, comparing values, computing statistics, and finding top/bottom rows. No obvious missing operation for the stated domain.