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Glama

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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful context beyond those annotations: no API key is needed and the result is a monthly time series. It does not mention possible data revisions or lags, but the annotation-covered safety profile lowers the burden and the added details are meaningful.

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?

The description is three focused sentences: a scoped subject, a concise indicator list, and a useful return/auth note. Every sentence earns its place and the most important identifying information is front-loaded.

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 tool with three optional parameters and no output schema, the description conveys the domain, supported indicators, monthly frequency, and keyless access. It could specify the exact time-series shape or units, but it is sufficient for an agent to select and invoke the tool correctly.

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%, with all three parameters already documented including defaults and the full indicator enumeration. The description restates the indicator list but adds no extra semantics such as date formats, units, or interactions between start_year and end_year.

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?

The description clearly identifies the data source (Bureau of Labor Statistics), the domain (US labor and price statistics), and the supported friendly-name indicators. It lacks an explicit action verb like 'get' or 'fetch' and does not explicitly distinguish itself from the sibling bls_series tool, so it falls just short of full differentiation.

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 guidance about when to use this tool versus alternatives like bls_series or FRED tools. The phrase 'by friendly name' and the indicator list imply the use case, but the description never states when not to use it or what to use instead for raw BLS series or different datasets.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.

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