BLS — Bureau of Labor Statistics

The U.S. Bureau of Labor Statistics's data warehouse. Employment, unemployment, wages, prices (CPI/PPI), productivity, occupational projections — the official US labor and price data. National, state, and metro level. Free with a registered API key.

Part of Pipeworx — an MCP gateway connecting AI agents to 1504+ live data sources.

Why this matters for AI agents

Anything labor-market or inflation-related at the official level: BLS. Where FRED gives you headline series, BLS gives you the underlying detail (occupation-level, industry-level, MSA-level). Agents researching local economic conditions, sector wages, or specific CPI components reach BLS directly.

Three core flows:

1. Look up a series. "What's the metro Denver unemployment rate?" → bls_search({query: "Denver unemployment"}) → series IDs. Then bls_get_series({series_id}) for values.

2. Latest value. "What's national unemployment right now?" → bls_latest({series_id: "LNS14000000"}) → most recent observation.

3. Browse popular series. "What does BLS publish?" → bls_popular_series → curated list of high-traffic IDs.

4. Local-area unemployment by place name. "What's the unemployment rate in Wake County, NC?" or "Cary, North Carolina" or "Fargo-Moorhead MSA" → bls_local_unemployment({place, state?}). This is the tool for LAUS (Local Area Unemployment Statistics) lookups by county / metro area / city — bls_search's curated catalog has no local-area entries, and constructing a LAUS series ID by hand requires a place → BLS area-code crosswalk (~8,000 US counties/metros/cities) that isn't something to guess at. bls_local_unemployment resolves the name against that crosswalk itself (bundled from BLS's la.area reference file), returns candidates when the name is ambiguous, and falls back to the county — saying so explicitly — when a city is below the LAUS city-reporting threshold.

Reading the response

bls_get_series returns each series newest first — the payload says so in observation_order, so you don't have to infer the direction from the dates. count is the number of points for that series, and BLS returns every point in the requested year range, so it is never a total sitting beside a shorter list.

Auth

BLS API requires a free key from https://www.bls.gov/developers/. Without it, calls are throttled to ~25/day per IP. With a key, it's 500/day. Pass via _apiKey per call.

Series ID structure

BLS series IDs encode survey, area, sector, and data type. Examples:

Series ID	What it is
`LNS14000000`	National unemployment rate (seasonally adjusted)
`CES0000000001`	Total nonfarm employment (national)
`CUUR0000SA0`	CPI all items, US city average, not seasonally adjusted
`LAUMT080000000000003`	Denver MSA unemployment rate
`WPSFD49207`	PPI for finished goods

Don't try to construct national/state/industry IDs from scratch — use bls_search or bls_popular_series. For LAUS local-area IDs (county/MSA/city) specifically, use bls_local_unemployment({place, state?}) — it resolves the place name to the correct area code for you.

Update cadence

Data	Release timing
Employment situation (national + state)	First Friday of the month
CPI	Mid-month (around the 10th-15th)
PPI	Mid-month, day after CPI
Metro unemployment (LAUS)	~3 weeks after the reference month
Productivity	Quarterly, ~5 weeks after quarter end

Pipeworx caches BLS responses with TTLs aligned to release schedules.

Common pitfalls

Seasonal adjustment. Same series exists in seasonally-adjusted (SA) and not-seasonally-adjusted (NSA) variants. Check the series ID prefix (LNS = SA, LNU = NSA). Mixing them is the most common analyst error.
Annual averages vs monthly. Some IDs return only annual data; others return monthly. Read the metadata before plotting trends.
State and metro coverage. Not every series exists at every geography. Smaller metros have larger lags and more imputed values.
Inflation vs CPI. "Inflation" usually means CPI year-over-year change. To get YoY, request 13 months and compute, or use FRED's CPIAUCSL with the pc1 units transformation (which does it for you).

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

config.json

{
  "mcpServers": {
    "bls": {
      "url": "https://gateway.pipeworx.io/bls/mcp"
    }
  }
}

What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/bls/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

config.json

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1504+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

Code

ask_pipeworx({ question: "your question about Bls data" })

The gateway picks the right tool and fills the arguments automatically.

License

MIT

Bls README