dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Lugtorq dataset. Call this first to learn the schema.
This tool declares no required parameters. Add optional parameters from the input schema as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {},
"name": "dataset_columns"
}
}| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of explaining behavior. It discloses what information is returned and that the tool is meant to be called first, but it does not explicitly state that the operation is read-only or describe any limitations or error conditions. For a zero-parameter metadata tool, this is adequate but not rich.
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 no wasted words: the first defines the output, the second gives a clear ordering directive. The most actionable guidance is placed at the end as an imperative, making the description easy to parse.
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 tool with no output schema and no annotations, the description tells the agent what it will receive and when to invoke it. It does not describe return formatting or edge cases, but for a simple schema-introspection tool this is sufficient.
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 no parameters, so there is no parameter ambiguity and the baseline is 4. The description focuses entirely on the output contents, which is appropriate here.
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 exposes the columns, numeric flags, row count, and provenance banner of the Lugtorq dataset, and the title adds 'shape' as the organizing concept. It is specific about the resource and content, though it does not explicitly delineate how this differs from the sibling dataset_provenance tool.
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 explicit ordering guidance for when to use the tool. It does not name alternatives or exclusions, but for an initial schema-discovery tool this is sufficient practical context.
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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