Retrieve nonfarm payroll data from the Alpha Vantage API to analyze U.S. employment trends for financial market insights.
Fetch nonfarm payroll
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": "nonfarm_payroll"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| datatype | No |
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 full burden for behavioral disclosure. 'Fetch' implies a read operation, but it doesn't specify data sources, update frequency, rate limits, authentication needs, or output format. This is inadequate for a tool with no structured behavioral hints.
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 extremely concise with just two words, front-loaded and zero waste. However, this conciseness comes at the cost of under-specification, but per scoring rules, it earns full points for brevity and structure.
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 no annotations, 0% schema coverage, no output schema, and a vague purpose, the description is completely inadequate. It doesn't provide enough context for an agent to understand what the tool does, how to use it, or what to expect, especially among many economic data siblings.
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 input schema has 1 parameter ('datatype') with 0% description coverage, and the tool description adds no information about parameters. It doesn't explain what 'datatype' means, possible values, or how it affects the fetch operation, failing to compensate for the schema's lack of documentation.
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 'Fetch nonfarm payroll' restates the tool name 'nonfarm_payroll' with a generic verb 'fetch', making it tautological. It doesn't specify what resource is being fetched (e.g., economic data, historical values, current figures) or distinguish it from sibling tools like 'unemployment' or 'cpi' that might fetch related economic indicators.
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?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for economic data (e.g., 'unemployment', 'cpi', 'real_gdp'), the description doesn't indicate context, prerequisites, or exclusions, leaving the agent without direction on appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.