dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the LLC Lane 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?
With no annotations, the description carries the behavioral disclosure burden. It conveys a read-only schema/health inspection ('learn the schema') and lists the metadata returned, but it doesn't explicitly state that no data is modified or describe output format or edge cases such as a missing dataset.
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 short sentences with the key return contents front-loaded and the usage instruction in the second sentence. No filler.
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 schema/shape tool with no parameters and no output schema, the description names the outputs the agent needs (columns, numeric columns, row count, provenance banner) and gives a clear first-step instruction. It is complete enough to invite the tool correctly in a workflow.
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 takes no parameters, so there are no parameter semantics to document; the baseline for 0-param tools is 4. The dataset name embedded in the description provides context that effectively identifies the sole implicit input.
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?
Identifies exactly what the tool exposes — columns, numeric flags, row count, and provenance banner — for the LLC Lane dataset, and signals its role as the schema-introspection entry point. It is clear and specific, though it never states an explicit verb like 'returns/provides' and doesn't name a sibling alternative.
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 imperative 'Call this first to learn the schema' gives an explicit use case and ordering relative to sibling dataset tools. It does not spell out when this tool should not be used or which sibling to choose instead, leaving some inference to the agent.
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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