dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Carbikly 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 behavioral burden. It describes the returned content (columns, numeric flags, row count, provenance banner) but does not explicitly state whether the operation is read-only or has side effects. The 'learn the schema' wording implies a safe introspection call, but this is not made explicit.
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 deliver the complete message: the tool's output and when to call it. There is no redundancy, restatement of the title, or filler; every phrase earns its place.
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 or annotations, the description covers the essential facts: what fields are returned and that it should be called first. An explicit read-only note would strengthen it, but nothing critical is missing given the low complexity.
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 schema coverage is trivially 100%. The description adds context about what the tool reveals, which is sufficient given that there are no parameter semantics to explain.
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 specifies the output: the Carbikly dataset's columns, which are numeric, the row count, and the provenance banner. The phrase 'learn the schema' establishes its unique role, though it does not explicitly contrast with sibling tools or use a strong verb like 'returns.'
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 description explicitly instructs to 'Call this first to learn the schema,' providing clear sequencing guidance for an agent. It does not name alternative tools or state when not to use it, so it lacks full when/when-not differentiation.
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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