dataset_infoCheck dataset coverage, verification date, claim count, methodology, and DOI archive before interpreting an absent claim as an answer.
Report what this dataset covers, when it was last verified, how many claims it holds, and where its methodology and DOI archive live. Read this before treating an absent claim as an answer.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does this well by surfacing a non-obvious caveat: an absent claim in the dataset should not be interpreted as a definitive answer without first checking this tool. It omits minor details like auth or return formatting, but those are less critical for a read-only metadata endpoint.
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 enumerates exactly what the tool reports, and the second delivers a practical usage warning. The structure is front-loaded and every sentence 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 tool with no parameters, no annotations, and no output schema, the description covers the essential return contents and the critical usage context. It could have explicitly described the response format, but the prose list of what the tool reports is sufficient for an agent to invoke it correctly and interpret its role.
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 there is no parameter semantics to explain. The baseline for a 0-parameter tool is 4, and the description appropriately focuses on the tool's purpose instead of inventing unnecessary parameter detail.
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 uses a specific verb ('Report') and names the exact resource (the dataset), then enumerates what the tool returns: scope, freshness, claim count, methodology, and DOI archive. This clearly differentiates it from the sibling tools about banking access and providers.
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 second sentence gives explicit guidance: call this before treating an absent claim as an answer, which tells the agent when its output is essential. It does not name sibling alternatives or state when not to use the tool, but the usage context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.