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bls_indicator

Read-onlyIdempotent

US labor & price statistics from the Bureau of Labor Statistics by friendly name. Available: unemployment_rate, labor_force_participation, employment_population_ratio, cpi, cpi_less_food_energy, nonfarm_payrolls, avg_hourly_earnings, avg_weekly_hours, ppi_final_demand. Returns a monthly time series. Keyless official BLS data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_yearNoEnd year (optional; defaults to current year).
indicatorNoIndicator name, one of: unemployment_rate, labor_force_participation, employment_population_ratio, cpi, cpi_less_food_energy, nonfarm_payrolls, avg_hourly_earnings, avg_weekly_hours, ppi_final_demand.
start_yearNoStart year (optional; defaults to ~3 years back).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / end_year / type
      Previous value: -"number"New value: +"integer"
    • changedInput schema / properties / start_year / type
      Previous value: -"number"New value: +"integer"
  2. First observed

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive, open-world behavior, so the safety profile is covered. The description adds real value beyond that: it discloses the return shape (a monthly time series) and the authentication posture (keyless, official BLS data), both of which the annotations do not convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short, front-loaded sentences with no filler; the capability statement comes first and the operational details (indicator list, return shape, keyless access) follow compactly. Minor redundancy between the indicator list here and in the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does state that a monthly time series is returned, plus that no key is required. It omits units/seasonal-adjustment conventions, which would be helpful for a statistics tool, but nothing critical to calling it is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the indicator list and the start/end year defaults are already documented in the schema. The description repeats the indicator enumeration but adds no syntax, unit, or frequency detail beyond it, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (US labor & price statistics from BLS) and enumerates the nine friendly indicator names, making the scope concrete. It does not explicitly name the nearest sibling (bls_series) or explain how the friendly-name lookup differs from a series-ID lookup, so sibling differentiation is only implied.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use or when-not guidance and no named alternative, despite several overlapping siblings (bls_series, fred_quick_indicator, fred_observations) that an agent must choose between. The indicator list implies usage but does not help the agent route correctly.

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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