Returns the top baby names aggregated across an entire calendar decade.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"decade": "<decade>"
},
"name": "get_decade_names"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| decade | Yes | Decade label, e.g. "1980s" |
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 full burden for behavioral disclosure. It indicates a read operation via 'Returns' but does not disclose result size, ordering, gender filtering, or whether 'top' is a fixed number. This is minimal viable transparency for a simple read tool.
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 a single sentence, front-loaded with the verb 'Returns', and contains no redundant words. Every element earns its place, making it highly concise and well-structured.
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 with one well-defined parameter, but no output schema exists and there is no mention of return value details (e.g., fields, limits). Given the presence of many sibling tools, the description lacks sufficient context on when to use it versus alternatives, making it only adequately complete for basic invocation.
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?
Schema description coverage is 100%; the single parameter 'decade' is already well-described with an example ('1980s'). The description adds no extra parameter semantics, so baseline 3 is appropriate even though it does not repeat what the schema already provides.
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 ('Returns') and clearly identifies the resource ('top baby names') and scope ('entire calendar decade'). This distinguishes it from sibling tools like get_year_names, which likely returns yearly data, making the purpose unmistakable.
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 implies usage for decade-level aggregation (e.g., 'aggregated across an entire calendar decade') but provides no explicit guidance on when to prefer this tool over alternatives like get_year_names or search_names. There are no exclusions or alternative tool mentions, leaving selection partially to inference.
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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