dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the MultiplesBook 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, the description carries the full burden of behavioral disclosure. It accurately describes the returned metadata content and implies a read-only introspection call, but it does not explicitly state that it is safe/non-destructive or describe potential failure modes.
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 two sentences with no filler. The output contents are front-loaded and the usage instruction is placed in a separate, clear sentence.
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, no-output-schema metadata tool, the description covers what data is returned and when to call it. It could optionally mention the exact return format, but that is not essential for correct invocation.
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 for the description to add beyond the schema. The baseline of 4 applies because no parameter documentation is needed.
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 that the tool returns columns, numeric flags, row count, and provenance banner for the MultiplesBook dataset, and it explicitly frames the tool as the way to learn the schema. It is specific and actionable, though it does not explicitly contrast itself with sibling tools like dataset_stats or dataset_provenance.
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 instruction 'Call this first to learn the schema' gives clear temporal guidance on when to use the tool. It does not mention exclusions or alternatives, but the usage context is explicit and sufficient for an agent.
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 target distinct query types: schema, provenance, exact row lookup, search, comparisons, stats, and top/bottom. However, dataset_row, dataset_search, and dataset_compare have some functional overlap around filtering rows, which could lead to selection mistakes.
All tools follow a consistent dataset_<operation> pattern with clear nouns describing the operation. This makes the tool set predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct common question type without unnecessary proliferation.
The surface covers the main ways users would query the dataset: schema, exact matches, substring search, comparisons, stats, and top/bottom. A minor gap is the lack of a simple 'get all rows' or pagination tool, but that is not required for the apparent Q&A purpose.