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LiveDataLink

by io.github.blackboxfoundry

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).

TDQS

A4/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the bar for extra context is lower. The description adds genuinely useful disclosure beyond annotations: 'Returns a monthly time series' and 'Keyless official BLS data,' which tells the agent no API key is needed and what periodicity to expect.

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

Conciseness5/5

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

Three compact sentences front-load the source and key qualifier, then list the valid indicators, and then state the output shape. Every sentence earns its place and there is no filler or redundancy.

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?

For a simple read-only, keyless lookup with optional parameters, the description covers source, available indicator names, return frequency, and auth requirements. It could add units or seasonal-adjustment details, but nothing critical to selecting or invoking the tool 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 schema already documents all three parameters and duplicates the indicator enum in text. The description adds no new meaning about start_year or end_year semantics beyond the schema, so baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the resource (US labor & price statistics from the Bureau of Labor Statistics), the delivery mode (by friendly name), and the return shape (monthly time series). The explicit list of supported indicators distinguishes it from a generic stats tool, and 'by friendly name' hints at the separation from the sibling bls_series tool.

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

Usage Guidelines3/5

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

The description implies the tool is for querying known friendly-named indicators, and the enumerated list shows valid options. However, it never explicitly names bls_series as the alternative for raw series IDs or states when not to use this tool, so routing between the two is left 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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TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources