BLS OEWS
PYPI · BLS-OEWS-MCP · 2 COMPONENTS
BLS Occupational Employment and Wage Statistics market wages, bundled; no API key. 8 tools.
Available components
Last scanned (6 hours ago)
Scores higher than 94% of PyPI servers
Category Data and Analytics
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 33 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to 1102tools-dev/federal-contracting-mcps). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 8 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability64
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1486 tokens (~185/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage71
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 4 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 28 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 27 Oct 26 −14
- Malware scan: pass → unverified ▼ security
- Stability: 0.97 → pass security
- Package version: 1.1.5 → 1.1.6 functional
- 26 Oct 26 +1
- Stability: fail → 0.97 functional
- 21 Oct 26 0
- Stability: fail → unverified ▼ security
- Tool safety: pass → unverified ▼ security
- Tool coverage: 100 → unverified ▼ functional
- Capabilities: pass → unverified ▼ functional
- Package version: 1.1.4 → 1.1.5 functional
- 20 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 84 to 88.
- 19 Oct 26 −1
No change was recorded against any check on this day. Stability & Change Management went from 88 to 84.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/bls-oews-mcp@1.1.6
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | pypi |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | 1102tools-dev/federal-contracting-mcps |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/1102tools-dev/federal-contracting-mcps/.github/workflows/publish-pypi.yml@refs/tags/v1.0.35 |
| Rekor log index | 3527332863 |
| Predicate type | PyPI publish attestation https://docs.pypi.org/attestations/publish/v1 |
| Subject digest | sha256:07f8d00953a4d9aaa6cc4362f3485b4f9b5589f57d56528c053406a6c0fa71c9 |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 33 packages
| Packages resolved | 33 |
|---|---|
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_metros ~128
Compare wages for one occupation across multiple metro areas. Pass a list of 5-digit MSA codes (e.g., ['47900', '42660', '12580'] for DC, Seattle, Baltimore). Returns the specified wage measure for each metro. Use list_common_metros() to find codes. datatype: '04' (Annual Mean, default), '13' (Median), '03' (Hourly Mean). Up to 50 metros per call.
| Name | Type | Req | Description |
|---|---|---|---|
| datatype | – | – | – |
| metro_codes | array | yes | – |
| occ_code | – | yes | – |
| year | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
compare_occupations ~88
Compare wages across multiple occupations in one location. Pass a list of 6-digit SOC codes. Returns the specified wage measure for each occupation. Use list_common_soc_codes() to find codes. Up to 50 occupations per call.
| Name | Type | Req | Description |
|---|---|---|---|
| area_code | – | – | – |
| datatype | – | – | – |
| occ_codes | array | yes | – |
| scope | string | – | – |
| year | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
detect_latest_year ~59
Report the OEWS data year this server answers from. OEWS releases annually in spring, and this server bundles the current release. Every tool defaults to that year. When BLS publishes a newer release, a new package version bundles it.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_data_status ~63
Report which OEWS release this server answers from and where it came from. Returns the bundled data year and release, the BLS source files with their SHA-256 and publication dates, the retrieval date, and row counts. No API key is used or needed.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_wage_data ~597
Get wage data for an occupation by SOC code. This is the primary tool for querying BLS OEWS wage statistics. occ_code: 6-digit SOC code without dash (e.g., '151252' for Software Developers, '131082' for Project Management Specialists). See list_common_soc_codes() for common mappings. scope + area_code: - 'national': no area_code needed (all US) - 'state': area_code = 2-digit state FIPS (e.g., '51' for VA, '11' for DC) - 'metro': area_code = 5-digit MSA code (e.g., '47900' for DC metro, '42660' for Seattle). See list_common_metros() for codes. industry: 6-digit industry code for national-only breakdowns. '000000' = all industries (default). Common: '541000' (Professional, Scientific, and Technical Services), '541500' (Computer Systems Design and Related Services), '999100' (Federal Executive Branch (OEWS Designation)). Industry breakdowns only work with scope='national'. Federal IT staff mostly fall under 15-1299 (Computer Occupations, All Other), so 999100 with 15-1252 rests on few employees. datatypes: list of 2-digit codes. Default uses IGCE set: - '04' = Annual Mean Wage - '13' = Annual Median - '11' = Annual 10th Percentile - '15' = Annual 90th Percentile Hourly percentiles: '03' (Mean), '06' (10th), '07' (25th), '08' (Median), '09' (75th), '10' (90th). Annual percentiles: '11' (10th), '12' (25th), '13' (Median), '14' (75th), '15' (90th). Other: '01' (Employment); '16' (Employment per 1,000 Jobs) and '17' (Location Quotient) exist at state/metro scope only. '02' (Employment RSE) and '05' (Mean Wage RSE) are relative standard errors in percent, a measure of each estimate's reliability. Data year defaults to the OEWS release bundled with this server (get_data_status reports it). Do NOT pass the current calendar year: OEWS publishes about a year in arrears. Other years are rejected. Special values ('-', '*', '#') mean BLS did not publish the cell; the attached footnote says why (annual-only occupation, sample too small).
| Name | Type | Req | Description |
|---|---|---|---|
| area_code | – | – | – |
| datatypes | – | – | – |
| industry | – | – | – |
| occ_code | – | yes | – |
| scope | string | – | – |
| year | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
igce_wage_benchmark ~391
Get wage benchmarks formatted for IGCE development. Returns annual and hourly wages at the mean and the 10th, 25th, median, 75th, and 90th percentiles (25th/75th are the usual junior/senior anchors), plus estimated burdened hourly rates using the specified burden multiplier range. Hourly figures are BLS's published hourly wages (annual / 2080 only where BLS publishes no hourly wage). Also returns the cell's employment and relative standard errors (reliability) and flags thin estimates. Wages are point-in-time estimates for the OEWS reference month (wage_period in the response), not current rates: escalate them to the period of performance before pricing. BLS wages are BASE wages (no fringe, overhead, G&A, or profit). Multiply by a burden factor to estimate fully-loaded rates: - 1.5x-1.7x: lean contractor - 1.8x-2.2x: mid-range professional services (default) - 2.0x-2.5x: large contractor with clearance overhead - 2.5x-3.0x: high-overhead (SCIF, deployed) The burdened range should roughly align with GSA CALC+ ceiling rates for comparable labor categories. If CALC+ >> burdened BLS, the role may require specialized skills or clearance overhead. Document the gap. Burden multipliers must be positive and burden_low <= burden_high. Reasonable range: 1.3 (lean) to 4.0 (high-overhead/clearance).
| Name | Type | Req | Description |
|---|---|---|---|
| area_code | – | – | – |
| burden_high | number | – | – |
| burden_low | number | – | – |
| occ_code | – | yes | – |
| scope | string | – | – |
| year | – | – | – |
Structured output declared, but exposes no named fields.
No examples provided.
list_common_metros ~79
List common metro area MSA codes for wage lookups. Use these codes with get_wage_data(scope='metro', area_code=...). Pass the 5-digit MSA code (the tool auto-pads to 7 characters). For the full MSA list: https://www.bls.gov/oes/current/msa_def.htm
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
list_common_soc_codes ~81
List common SOC code mappings for federal IT and professional services. Use these codes with get_wage_data() and other tools. SOC codes are 6 digits without a dash (e.g., '151252' not '15-1252'). For the full SOC list: https://www.bls.gov/oes/current/oes_stru.htm
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the BLS OEWS MCP server?
BLS OEWS runs locally as a PyPI package, launched with uvx bls-oews-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · bls-oews-mcp
claude mcp add com-1102tools-bls-oews-mcp -- uvx bls-oews-mcp
What is the BLS OEWS MCP server?
BLS OEWS is an MCP server listed in the public MCP registry as com.1102tools/bls-oews-mcp. BLS Occupational Employment and Wage Statistics market wages, bundled; no API key. 8 tools. This page covers its PyPI package (bls-oews-mcp).
Is the BLS OEWS MCP server safe to use?
BLS OEWS scores 91 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the BLS OEWS MCP server expose?
BLS OEWS exposes 8 tools: get_data_status, get_wage_data, compare_metros, compare_occupations, igce_wage_benchmark, and 3 more. Their descriptions and schemas cost roughly 1,486 tokens of context every time the server is loaded.
Is the BLS OEWS MCP server still maintained?
BLS OEWS is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the BLS OEWS MCP server under?
BLS OEWS declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.