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by io.github.pipeworx-io

Bls Local Unemployment

bls_local_unemployment
Read-onlyIdempotent

Look up the current LOCAL-AREA unemployment rate (or employment/labor force) for a US county, metro area (MSA), or city/town by NAME — e.g. "Wake County, NC", "Cary, North Carolina", "Fargo-Moorhead MSA". Resolves the place to its BLS LAUS series id (bls_get_series cannot do this; it needs a series ID, not a place name) and returns recent monthly data. Falls back to the county when a city is below the LAUS city-reporting threshold, and says so. Data is NOT seasonally adjusted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
placeYesPlace name: a county ("Wake County"), metro area ("Fargo-Moorhead MSA", "Raleigh-Cary MSA"), or city/town ("Cary"). Include "County" or "MSA"/"Metro" in the name to disambiguate the kind when it matters.
stateNoUS state, 2-letter code or full name (e.g. "NC" or "North Carolina"). Optional if already included in `place` (e.g. "Cary, NC").
monthsNoHow many most-recent monthly observations to return. Default 12.
_apiKeyNoBLS registration key (optional, increases rate limits)
measureNoOne of: "rate" (unemployment rate, default), "unemployment" (level), "employment" (level), "labor_force" (level).

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds meaningful behavioral context beyond that: it resolves place names to LAUS series IDs, falls back to county-level data when a city is below the reporting threshold, and explicitly notes the data is not seasonally adjusted.

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 compact and well-structured: three sentences with no filler. The first sentence establishes purpose and scope, the second covers resolution and fallback, and the third states the key data caveat. Every sentence earns its place.

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?

The description covers the tool's scope, resolution mechanism, fallback behavior, and seasonal-adjustment caveat, which is strong for a simple read tool. However, since there is no output schema, the phrase 'returns recent monthly data' is somewhat thin on the actual return structure, units, or field names an agent should expect.

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 coverage is 100%, and the schema already documents each parameter with examples. The description reinforces the place/state format with examples like 'Wake County, NC' and 'Fargo-Moorhead MSA', but it does not add meaningful semantics beyond what the schema already provides.

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 opens with a specific action and resource: 'Look up the current LOCAL-AREA unemployment rate... for a US county, metro area (MSA), or city/town by NAME.' It gives concrete examples and explicitly distinguishes itself from bls_get_series, making the tool's purpose unmistakable.

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

Usage Guidelines5/5

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

It explicitly states that bls_get_series cannot resolve place names and requires a series ID, which tells the agent when to prefer this tool over that sibling. It also discloses the fallback behavior for small cities and the non-seasonal-adjustment caveat, providing clear usage context.

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

A3.6/5.0
Disambiguation2/5

The ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded trio are nearly indistinguishable, with beta currently behaving identically to the stable version. Entity_profile, recent_changes, and compare_entities also overlap heavily as multi-source company research tools, and the six polymarket tools create additional boundary confusion.

Naming Consistency2/5

Most tools use snake_case, but there is no consistent verb_noun pattern: some are imperative phrases (ask_pipeworx, bls_get_series, resolve_entity), while others are noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, recent_alerts). Even within the bls_* family, bls_latest breaks the verb pattern established by bls_get_series and bls_search.

Tool Count2/5

35 tools is well above the 25+ threshold for 'too many', and the server is named Bls yet only four tools actually serve BLS data. Most of the remaining tools cover unrelated domains like Polymarket arbitrage, memory, feedback, and general Pipeworx routing, making the count feel inflated for the apparent purpose.

Completeness3/5

The four BLS-specific tools cover search, browse, historical series fetch, and latest value, but there is no multi-series fetch or series metadata detail, which is a notable gap for a BLS-focused server. The broader Pipeworx toolset is extensive, but the lack of a coherent stated domain makes completeness hard to evaluate as a unified surface.

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