US labor-market signal: monthly jobs added (nonfarm payrolls), unemployment rate, wage growth, JOLTS job openings and quits, and labor-force participation. BLS data.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / $schemaAdded value: +"http://json-schema.org/draft-07/schema#"Input schema / additionalPropertiesRemoved value: -falseOutput schema / (root)Previous value: -{
- "additionalProperties": true,
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the data source (BLS) and lists the included metrics, but omits critical details like the output format (snapshot vs. time series), update frequency, units, or any caveats. This is a minimal disclosure that does not fully inform an agent about what to expect from the invocation.
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, well-structured sentence. It front-loads the primary purpose ('US labor-market signal') and then lists the specific data points. Every word adds value with no redundancy or filler.
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 (no parameters) and absence of an output schema, the description is moderately complete. It enumerates the metrics but does not specify the return structure (e.g., an object with keys), units, or whether historical series are included. This leaves some ambiguity for an agent attempting to interpret the result.
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 is empty (0 parameters), so there are no parameter semantics to document. Per the evaluation rubric, 0 params grants a baseline of 4. The description correctly avoids redundantly describing parameters.
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 identifies the tool as a US labor-market signal and enumerates five specific metrics (nonfarm payrolls, unemployment rate, wage growth, JOLTS openings/quits, participation). This makes the tool's purpose concrete and distinguishes it from siblings like equity_signal or token_scan, which cover unrelated domains.
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 labor-market data but does not explicitly state when to choose this tool over alternatives. There are no exclusions or guidance on context (e.g., 'use for macro labor analysis'). The clear scope and zero parameters make it obvious enough, but explicit guidance is absent.
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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