Overview

US labor statistics from the Bureau of Labor Statistics public API v2 and LABSTAT flat-file catalog. Resolve opaque SeriesIDs from natural language, fetch historical time-series or the latest observation, and query large multi-series results with SQL through an optional DataCanvas. Runs as a stdio process, a local Streamable HTTP server, or the public hosted endpoint above.

Tools

Capability reference

bls_list_surveys _tool

• Optional category filters survey programs by subject; returns codes, names, and calculation-support flags (allowsNetChange, allowsPercentChange, hasAnnualAverages).
• Backed by the BLS surveys API with monthly caching. Annual-average support is advisory; read bls_get_series's annualAverageRows for the rows actually returned.

bls_search_series _tool

• Search by text or SeriesID, with optional survey, area (substring of area, title, or SeriesID), and seasonal_adjustment; blank filters are omitted. limit is 1–50 (default 10), with offset pagination; without an area filter, equally matching national series rank first.
• Returns decoded SeriesIDs, titles, and frequency. Follow nextOffset while truncated is true; capped: true makes totalCount a lower bound, and paging stops at the FTS candidate pool.
• Searches the offline LABSTAT index without using API quota; OE/OEWS is opt-in through BLS_CATALOG_INCLUDE_OES. Unindexed surveys remain fetchable by SeriesID.

bls_get_series _tool

• Fetch 1–50 SeriesIDs per call, with a start_year/end_year window of up to 20 years, optional calculations, and annual_average. A start alone resolves the end to the current year capped at start + 19; an end alone is rejected. One batch consumes one API query; calculations depend on survey support (CPI/PPI return percent change only).
• Returns observations with available, preserves the raw - missing-value sentinel, and keeps valid series in mixed batches. Enrichment reports the applied year window, calculations, and annual-average rows.
• Large results spill to dataset.name when CANVAS_PROVIDER_TYPE=duckdb; otherwise canvas_unavailable asks for a narrower window.

bls_get_latest _tool

• Fetch the latest observation for up to 50 SeriesIDs (recommended ≤10). Each ID consumes one API query; use bls_get_series for a more efficient multi-series batch.
• Returns successful results[] beside per-series failed[] entries. latestObservation.available distinguishes a published value from BLS's missing-value sentinel.

bls_dataframe_describe _tool

• Optional name describes one registered dataframe; omit it to list active dataframes for the tenant. Requires CANVAS_PROVIDER_TYPE=duckdb.
• Returns source tool, query params, row count, TTL (created_at/expires_at), and column_schema; BLS columns are nullable. Registered-query types outside the canvas set (DECIMAL, HUGEINT, SMALLINT, lists) report as VARCHAR. Expired entries are removed when their canvas drop succeeds.

bls_dataframe_query _tool

• Run one SELECT against registered tables, with JOINs, aggregates, window functions, or CTEs. row_limit defaults to 1000 and caps at 10000; writes, external-file reads, and system catalogs are denied.
• Returns rows without consuming BLS API quota. Optional register_as stages the result with a fresh TTL for chained analysis; requires CANVAS_PROVIDER_TYPE=duckdb.

bls_dataframe_drop _tool

• Required name identifies the dataframe to drop. Available only with CANVAS_PROVIDER_TYPE=duckdb and BLS_DATAFRAME_DROP_ENABLED=true; TTL handles cleanup by default.
• Returns dropped: false for a missing table. A failed drop returns retryable canvas_drop_failed and leaves the dataframe registered.

Features

Built on @cyanheads/mcp-ts-core: stdio and Streamable HTTP transports, pluggable auth (none / jwt / oauth), swappable storage (in-memory, filesystem, Supabase, Cloudflare KV/R2/D1), structured logging with optional OpenTelemetry tracing.

BLS-specific:

• BLS API v2 client with retry/backoff and daily quota tracking
• Offline series catalog search against LABSTAT flat files, indexed as an on-disk SQLite/FTS5 store — zero API quota for discovery; the OES/OEWS wage survey (~6M series) is opt-in via BLS_CATALOG_INCLUDE_OES
• Typed error contracts for BLS-specific failure modes — quota exhaustion, locked database, calculations not supported
• Period-over-period net/percent-change calculations via BLS's own server-side flag, consistent with BLS's published numbers
• Optional DataCanvas spillover (DuckDB) for large multi-series result sets — schema discovery and SQL access without re-querying the API

Agent-friendly output:

• Provenance — canvas-spilled results carry a dataset.name handle plus row count and expiry; bls_search_series echoes effectiveQuery, catalogSize, and whether the FTS candidate pool was capped
• Graceful partial failure — bls_get_latest returns per-item failed[] (seriesId + error) alongside successful results[] instead of failing the whole batch; bls_get_series keeps valid series when another SeriesID in the same batch is invalid or empty
• Discriminated outputs — every observation carries an available boolean for BLS's - missing-value sentinel, so callers branch on a typed field instead of parsing raw values
• Actionable notices — enrichment.notice explains empty results, canvas spillover, and unavailable data with concrete next steps (e.g. using bls_search_series to verify a SeriesID)

Getting started

Public Hosted Instance

A public instance is available at https://bls-labor.caseyjhand.com/mcp — no installation required. Point any MCP client at it via Streamable HTTP:

{
  "mcpServers": {
    "bls-labor-mcp-server": {
      "type": "streamable-http",
      "url": "https://bls-labor.caseyjhand.com/mcp"
    }
  }
}

Self-Hosted / Local

Add the following to your MCP client configuration file. A free BLS API key unlocks 500 queries/day — register at bls.gov/developers. The server works without a key at 25 req/day.

{
  "mcpServers": {
    "bls-labor-mcp-server": {
      "type": "stdio",
      "command": "bunx",
      "args": ["@cyanheads/bls-labor-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info",
        "BLS_API_KEY": "your-key-here"
      }
    }
  }
}

Or with npx (no Bun required):

{
  "mcpServers": {
    "bls-labor-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@cyanheads/bls-labor-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "MCP_LOG_LEVEL": "info",
        "BLS_API_KEY": "your-key-here"
      }
    }
  }
}

Or with Docker:

{
  "mcpServers": {
    "bls-labor-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "MCP_TRANSPORT_TYPE=stdio", "-e", "BLS_API_KEY=your-key-here", "ghcr.io/cyanheads/bls-labor-mcp-server:latest"]
    }
  }
}

For Streamable HTTP, set the transport and start the server:

MCP_TRANSPORT_TYPE=http MCP_SESSION_MODE=stateless MCP_HTTP_PORT=3010 BLS_API_KEY=... bun run start:http
# Server listens at http://localhost:3010/mcp

Prerequisites

• Bun v1.4.0 or higher (or Node.js v24+).
• A free BLS API v2 key — register at bls.gov/developers. Grants 500 queries/day; the server also works without a key at 25 req/day.

Installation

1. Clone the repository:

git clone https://github.com/cyanheads/bls-labor-mcp-server.git

2. Navigate into the directory:

cd bls-labor-mcp-server

3. Install dependencies:

bun install

4. Configure environment:

cp .env.example .env
# edit .env and set BLS_API_KEY

Configuration

All configuration is validated at startup via Zod schemas in src/config/server-config.ts.

See .env.example for the full list of optional overrides.

Observation mirror (optional)

For high-volume workloads, an opt-in local mirror serves bls_get_series / bls_get_latest from an embedded SQLite store instead of the BLS API — eliminating the 500/day quota cap. It is off by default. To enable:

1. Set BLS_OBSERVATIONS_MIRROR_ENABLED=true (and review the BLS_OBSERVATIONS_MIRROR_* vars in .env.example).

2. Run the one-time bootstrap out-of-band — it downloads the full LABSTAT observation set and can take a while:

   shCopy

   node dist/services/bls-observations/subprocess.js --init
   

The mirror harvests every survey in the catalog's survey list — OE included, whatever BLS_CATALOG_INCLUDE_OES says — so it covers CM and CI too (about 55 MB of cm.data.* / ci.data.* files). An incremental refresh reads only files published after the mirror's last one, so a mirror bootstrapped before CM and CI joined picks each file up at its next BLS publication; until then those series come from the live API when fallback is on. Until the bootstrap completes, requests fall back to the live API (unless BLS_OBSERVATIONS_MIRROR_FALLBACK_LIVE=false). When a live fallback fails — quota exhausted, BLS unreachable — the series the mirror served are still returned, and enrichment.notice names each unserved SeriesID with the failure's reason and recovery. Mirror rows carry no BLS calculations: with calculations: true, a mirror-served series comes back without calculation fields, enrichment.calculationsApplied is false, and the notice names it. On HTTP transport, an incremental refresh runs on the BLS_OBSERVATIONS_MIRROR_REFRESH_CRON schedule. In containers, mount a persistent volume at BLS_OBSERVATIONS_MIRROR_PATH.

Upgrading an existing mirror. A mirror bootstrapped before sentinel rows were stored is missing the periods BLS publishes with its - missing-value marker. Opening such a mirror clears its sync checkpoint once, so the next refresh re-reads every LABSTAT file and fills them in — no operator action beyond letting that refresh run, and it takes as long as a full read. The mirror keeps serving throughout.

Running the server

Local development

• Build and run:

  shCopy

  # One-time build
  bun run rebuild
  
  # Run the built server
  bun run start:stdio
  # or
  bun run start:http
  

• Run checks and tests:

  shCopy

  bun run devcheck   # Lint, format, typecheck, security
  bun run test       # Vitest test suite
  bun run lint:mcp   # Validate MCP definitions against spec
  

Docker

docker build -t bls-labor-mcp-server .
docker run --rm -e BLS_API_KEY=your-key -e MCP_TRANSPORT_TYPE=http -p 3010:3010 bls-labor-mcp-server

The Dockerfile defaults to HTTP transport, stateless session mode, and logs to /var/log/bls-labor-mcp-server. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

Project structure

Development guide

See CLAUDE.md for development guidelines and architectural rules. The short version:

• Handlers throw, framework catches — no try/catch in tool logic
• Use ctx.log for request-scoped logging, ctx.state for tenant-scoped storage
• bls_search_series is the anchor tool — design workflows to call it before the API tools
• Wrap BLS API calls: validate raw → normalize to domain type → return output schema; never fabricate missing fields

Contributing

Issues are welcome. Run checks and tests before submitting:

bun run devcheck
bun run test

License

Apache-2.0 — see LICENSE for details.