dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Partnro 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 provided, the description must carry the full burden of behavioral disclosure. It does disclose the return contents (columns, numeric flags, row count, provenance banner) but does not explicitly state whether the operation is read-only or has any side effects. Given the nature of a schema-discovery tool, the description is adequate but not exhaustive about behavior beyond what it returns.
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 wasted words. The first sentence enumerates the exact outputs, and the second gives a clear usage directive. All content is relevant and front-loaded, making it easy for an agent to parse quickly.
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?
The tool is simple (no parameters, no output schema) and the description covers its purpose and the recommended invocation order. It might be slightly enhanced by stating the return format or that it is safe, but given the low complexity, the description is complete enough for an agent to use the tool correctly without ambiguity.
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 coverage is trivially 100%. With no parameters to document, the description adds no parameter-specific semantics but also has nothing to explain. The baseline for zero parameters is 4, and the description does not detract from that.
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 what the tool does: returns the columns, which are numeric, the row count, and the provenance banner for the Partnro dataset. It uses a specific resource ('Partnro dataset') and conveys a distinct scope, and the 'Call this first' hint differentiates it from sibling tools that likely focus on other aspects.
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' provides explicit guidance on when to use this tool, positioning it as the initial discovery step before other dataset tools. It does not explicitly list exclusions or alternatives, but the directive is clear enough for an agent to know this is the entry point.
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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