bls_series
Fetch any BLS data series. Available: unemployment_rate, cpi_all, cpi_core, nonfarm_payrolls, labor_participation, avg_hourly_earnings, labor_force.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | Series name or BLS series ID |
Fetch any BLS data series. Available: unemployment_rate, cpi_all, cpi_core, nonfarm_payrolls, labor_participation, avg_hourly_earnings, labor_force.
| Name | Required | Description | Default |
|---|---|---|---|
| series | No | Series name or BLS series ID |
Changes observed during successful MCP inspections.
Input schema / additionalPropertiesAdded value: +falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does not disclose what the tool returns, how errors are handled for invalid series, or any rate limits. The available list hints that unsupported names could be rejected, but that is left unstated.
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 with a clean list. It is front-loaded with the action and immediately provides usable details with no wasted words.
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 simple one-parameter fetch tool, the description provides the essential valid values but omits return format, potential errors, and any date/unit context. Given no output schema or annotations, this leaves gaps for the agent.
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 schema's description ('Series name or BLS series ID') is generic, but the tool description adds concrete valid series names, which is the only place enumerating options. This significantly aids correct parameter usage.
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 the tool fetches any BLS data series and enumerates specific available series. It is distinct from siblings like bls_employment or bls_inflation by being generic, though it does not explicitly differentiate itself.
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 list of available series implies when to use the tool, but there is no explicit guidance on choosing this over specialized siblings or what situations are inappropriate. No exclusions or alternatives are mentioned.
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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