dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Batesio 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 disclosure burden. It transparently indicates this is an inspection operation by saying 'Call this first to learn the schema' and enumerates the output content. It does not explicitly state that the operation is side-effect-free or describe error behavior, but the read-only nature is strongly implied by the described purpose.
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 first sentence lists exactly what the caller gets, and the second provides actionable usage guidance. Every word contributes to the agent's understanding.
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?
Given the low complexity, empty parameter schema, and no output schema, the description adequately enumerates the returned information and gives sequencing context. It could be more explicit about the output format or the meaning of 'numeric', but the essential information an agent needs before calling the tool is present.
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 and the schema description coverage is 100%, so no parameter explanation is required. The description adds no parameter-specific meaning, but there is nothing to compensate for.
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 the dataset's columns, numeric indicators, row count, and provenance banner, and it positions the tool as the first call for learning the schema. It does not explicitly name sibling tools, but the listed contents make the tool's purpose distinct from dataset_stats, dataset_provenance, and dataset_search.
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 gives explicit timing guidance: 'Call this first to learn the schema.' This tells an agent when to invoke the tool during exploration. It does not explicitly explain when not to use it or mention alternatives, but for a schema-introspection tool this is sufficient contextual direction.
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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