Workforce Competitive Intelligence MCP Server

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Quick Start

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

{
  "mcpServers": {
    "workforce-competitive-intelligence-mcp": {
      "url": "https://ryanclinton--workforce-competitive-intelligence-mcp.apify.actor/mcp"
    }
  }
}

---

Workforce competitive intelligence at query speed — this MCP server gives your AI assistant live access to hiring signals, patent inventor movement, researcher attrition, technical capability maps, and executive flight risk for any company. Connect it to Claude, Cursor, or any MCP-compatible client and ask questions that used to require a full analyst team.

The server orchestrates 7 independent data sources — job postings, USPTO patents, EPO patents, ORCID researcher profiles, company research, GitHub activity, and SEC Form 4 insider trading filings — running them in parallel and applying four scoring models to produce structured workforce intelligence. Every tool call returns a scored, signal-annotated JSON response with INVEST/MONITOR/CAUTION/AVOID verdicts. No subscription. No spreadsheet. Pay $0.045 per query.

What data can you extract?

| Data Point | Source | Example |
|---|---|---|
| 📋 Open job count, seniority mix, function distribution | Job Market Intelligence | 47 openings: 12 senior, 18 engineering roles |
| 🔬 Patent inventors, filing velocity, IP domains | USPTO Patent Search | 23 US patents, 8 unique inventors, AI/ML focus |
| 🌍 European patent applications, cross-border IP | EPO Patent Search | 14 EU patents, global IP strategy detected |
| 🎓 Researcher affiliations, mobility rate, ORCID profiles | ORCID Researcher Search | 6 of 19 researchers with multiple affiliations |
| 🏢 Company funding, expansion signals, distress indicators | Company Deep Research | Series C raised, headcount growth noted |
| 💻 GitHub repos, stars, contributor activity, topics | GitHub Repo Search | 31 repos, 4,200 stars, active in last 180 days |
| 📈 SEC Form 4 sell/buy ratio, large transactions | SEC Insider Trading | 9 insider sells vs 2 buys — 81% sell ratio |
| 🎯 Talent Velocity Score (0-100) | Composite scoring | Score 74 — SURGING growth signal |
| 🔴 Brain Drain Index (0-100) | Composite scoring | Score 42 — AT_RISK drain level |
| ⚠️ Executive Flight Risk (0-100) | Composite scoring | Score 68 — HIGH risk, 3 serial sellers |
| 🗺️ Competitive Capability Map, tech domains | Composite scoring | Dominant in AI/ML, Cloud, DevOps |
| 📊 Composite Workforce Dossier verdict | All 7 sources | INVEST — composite score 71 |

Why use Workforce Competitive Intelligence MCP Server?

Building a workforce intelligence picture manually means pulling job listings from five sources, cross-referencing USPTO and EPO patent filings, checking ORCID profiles, reviewing SEC EDGAR Form 4 filings, and combing GitHub for engineering signals. For a single company that is a full day's work. For a competitor comparison it is two days. For a portfolio of ten companies it simply does not happen.

This MCP server automates the entire process. Ask your AI assistant a single question — "What is the human capital risk for Palantir?" — and receive a scored, signal-annotated dossier in under two minutes, drawing on all 7 sources at once.

- Scheduling — run recurring workforce monitors daily or weekly so your AI receives fresh hiring signals without any manual trigger
- API access — trigger analyses from Python, JavaScript, or any HTTP client alongside your existing research pipelines
- Parallel execution — all 7 actor calls run concurrently so a full dossier completes in the time of a single sequential lookup
- Spending controls — set a per-run maximum charge so cost never exceeds what you authorize
- Integrations — connect results to Zapier, Make, webhooks, or your CRM for automated talent risk alerting

Features

- Talent Velocity Score (0-100) — composite of job posting volume (max 40 pts), hiring function diversity via HHI analysis (max 25 pts), GitHub activity from the last 180 days (max 20 pts), and company growth signals (max 15 pts)
- Brain Drain Index (0-100) — patent inventor continuity analysis across USPTO and EPO filings, ORCID multi-affiliation mobility rate, SEC insider sell ratio as a leadership flight proxy, and low-hiring combined signals
- Competitive Capability Map — maps 10 technology domains (AI/ML, Cloud, Blockchain, Cybersecurity, Mobile, Data/Analytics, Frontend, Backend, DevOps) across job titles, patent titles, and GitHub topics using keyword frequency scoring
- Executive Flight Risk (0-100) — SEC Form 4 sell/buy ratio (max 40 pts), serial seller concentration for named executives (max 20 pts), C-suite replacement hiring (CEO/CTO/CFO/COO/VP openings, max 20 pts), company distress text signals (max 20 pts)
- Composite Workforce Dossier — weighted composite of Talent Velocity (25%), inverted Brain Drain (25%), Competitive Capability (30%), and inverted Executive Flight Risk (20%) with INVEST/MONITOR/CAUTION/AVOID override logic
- AVOID override — if Executive Flight Risk is CRITICAL or Brain Drain is HEMORRHAGING the verdict is forced to AVOID regardless of composite score
- 7 parallel data sources — job postings, USPTO patents, EPO patents, ORCID researchers, company research, GitHub repos, and SEC insider transactions fetched simultaneously
- Signal narration — every score includes human-readable signal strings (e.g., "9 senior/leadership roles — building new org layers") for direct use in AI-generated reports
- Two-company benchmarking — benchmark_talent_strategy runs both companies in parallel and returns side-by-side Talent Velocity and Competitive Capability scores with a declared advantage winner
- Human capital risk composite — assess_human_capital_risk weights Brain Drain at 55% and Executive Flight Risk at 45% for a single organizational health number
- Growth signal classification — five-tier labels: CONTRACTING, STABLE, GROWING, SURGING, HYPERGROWTH applied to Talent Velocity output
- Drain level classification — five-tier labels: RETAINING, STABLE, AT_RISK, DRAINING, HEMORRHAGING applied to Brain Drain output
- Capability level classification — five-tier labels: NASCENT, DEVELOPING, COMPETITIVE, LEADING, DOMINANT applied to Competitive Capability output
- Pay-per-event pricing — $0.045 per tool call with no standing subscription

Use cases for workforce competitive intelligence

Competitor strategic pivot detection

Corporate strategy teams and competitive intelligence analysts need to know when a competitor is shifting direction months before the press release. An AI assistant connected to this MCP can answer "Is Palantir pivoting toward defense AI hiring?" with live job posting volume, seniority mix, and function distribution data. Sudden engineering concentration in AI/ML roles, combined with US and EU patent filings in the same domain, provides a 3-6 month early signal ahead of public announcements.

M&A human capital due diligence

Investment bankers, PE analysts, and corp dev teams running acquisition due diligence need to quantify human capital risk before close. The generate_workforce_dossier tool produces a scored dossier with INVEST/MONITOR/CAUTION/AVOID verdicts in under two minutes. Pair this with patent inventor movement data to assess whether key IP creators are still active or have departed for competitors, directly affecting the target's intangible asset valuation.

Executive retention early warning

Investor relations teams, board members, and HR leaders at public companies track SEC Form 4 insider trading as an executive stability signal. The monitor_executive_transitions tool identifies executives with high sell ratios, large individual transactions above $1M, and unusual concentration patterns (serial sellers) and flags C-suite replacement roles open simultaneously — a multi-signal indicator of imminent leadership instability.

Talent strategy benchmarking

Heads of talent acquisition and CHROs comparing their organization against a direct competitor need more than LinkedIn headcount numbers. The benchmark_talent_strategy tool runs both companies through parallel job posting, patent, and GitHub analysis and returns side-by-side Talent Velocity Scores and Competitive Capability Maps, identifying which company has the stronger hiring momentum and deeper technical domain presence.

Academic-to-industry talent monitoring

Research-intensive industries — pharma, defense, semiconductor, biotech — need to know when key academic researchers affiliate with commercial competitors. The detect_researcher_attrition tool queries ORCID profiles for multi-affiliation researchers (scientists with two or more institutional ties), cross-references active patent filings, and calculates a mobility rate that signals potential technology transfer risks.

Portfolio talent risk monitoring

Venture capital and private equity firms with 10+ portfolio companies cannot manually track workforce health for every holding. Schedule assess_human_capital_risk runs weekly for each company and route results via webhook to a Slack channel or CRM. Flag any holding that crosses an 60+ human capital risk threshold for proactive founder conversations before talent issues compound.

How to connect this MCP server

Step 1: Get your Apify API token

Sign up at apify.com and copy your token from Settings > Integrations. The Apify free plan includes $5 of monthly credits — enough for roughly 111 tool calls.

Step 2: Add the MCP server to your client

The server endpoint is:

https://workforce-competitive-intelligence-mcp.apify.actor/mcp

Use your Apify token for authentication.

Step 3: Configure your MCP client

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "workforce-competitive": {
      "url": "https://workforce-competitive-intelligence-mcp.apify.actor/mcp",
      "headers": {
        "Authorization": "Bearer YOUR_APIFY_TOKEN"
      }
    }
  }
}

Cursor / Windsurf / Cline — add the server URL and Authorization header in your MCP settings panel. The server responds to standard MCP protocol over HTTP POST.

Step 4: Start querying

Ask your AI assistant natural language questions:

- "Generate a full workforce dossier for Stripe"
- "Compare hiring velocity between Snowflake and Databricks"
- "What is the executive flight risk for Intel?"
- "Track inventor movement at NVIDIA"

MCP tools

| Tool | Price | Data Sources | Description |
|---|---|---|---|
| analyze_hiring_signals | $0.045 | Jobs + Company + GitHub | Talent Velocity Score with function distribution and growth signal |
| track_inventor_movement | $0.045 | USPTO + EPO | Inventor continuity, top inventor list, patent velocity |
| detect_researcher_attrition | $0.045 | ORCID + USPTO | Researcher mobility rate, affiliation changes, attrition signals |
| map_technical_capabilities | $0.045 | Jobs + USPTO + EPO + GitHub | Capability Map across 10 tech domains with patent and repo counts |
| monitor_executive_transitions | $0.045 | SEC + Company + Jobs | Executive Flight Risk score with sell/buy ratio and serial seller flags |
| benchmark_talent_strategy | $0.045 | Jobs + USPTO + GitHub (both companies) | Side-by-side Talent Velocity and Capability scores, advantage declaration |
| assess_human_capital_risk | $0.045 | USPTO + EPO + ORCID + SEC + Jobs | Brain Drain (55%) + Executive Flight (45%) composite risk score |
| generate_workforce_dossier | $0.045 | All 7 sources | Complete dossier with all 4 scoring models and INVEST/MONITOR/CAUTION/AVOID verdict |

Tool tips

- Use generate_workforce_dossier for unknown targets — it pulls all 7 sources and all 4 models, giving you the widest picture before narrowing with individual tools
- Use benchmark_talent_strategy before a competitive pitch — side-by-side scores give your team a defensible, quantified talking point
- Use assess_human_capital_risk for portfolio monitoring — it is the cheapest path to a single risk number suitable for threshold-based alerting
- Use track_inventor_movement for IP due diligence — the top inventors list shows exactly who has been creating IP and whether their output is declining
- Chain tools for deeper analysis — run monitor_executive_transitions first and if flight risk is HIGH, follow with detect_researcher_attrition to confirm whether the leadership instability is correlating with talent loss

Output example

The following is a representative response from generate_workforce_dossier for a mid-size technology company:

{
  "entity": "Meridian Analytics Inc",
  "compositeScore": 68,
  "verdict": "MONITOR",
  "talentVelocity": {
    "score": 74,
    "totalOpenings": 38,
    "seniorRoles": 11,
    "techRoles": 22,
    "growthSignal": "SURGING",
    "signals": [
      "38 open positions — aggressive hiring campaign",
      "11 senior/leadership roles — building new org layers",
      "22 technical roles — major engineering investment",
      "Hiring across 5 functions — broad organizational growth"
    ]
  },
  "brainDrain": {
    "score": 37,
    "inventorCount": 14,
    "researcherCount": 11,
    "patentActivity": 19,
    "drainLevel": "AT_RISK",
    "signals": [
      "4 researchers with multiple affiliations — talent mobility risk"
    ]
  },
  "competitiveCapability": {
    "score": 71,
    "techDomains": ["AI/ML", "Cloud", "Data/Analytics", "DevOps", "Backend"],
    "patentStrength": 19,
    "talentDepth": 24,
    "capabilityLevel": "LEADING",
    "signals": [
      "Active across 5 tech domains: AI/ML, Cloud, Data/Analytics, DevOps, Backend",
      "19 patents — strong IP portfolio",
      "Patents in both US and EU — global IP strategy",
      "24 public repos — significant open-source investment"
    ]
  },
  "executiveFlight": {
    "score": 29,
    "insiderSells": 4,
    "insiderBuys": 7,
    "sellRatio": 0.36,
    "riskLevel": "MODERATE",
    "signals": []
  },
  "allSignals": [
    "38 open positions — aggressive hiring campaign",
    "11 senior/leadership roles — building new org layers",
    "22 technical roles — major engineering investment",
    "Active across 5 tech domains: AI/ML, Cloud, Data/Analytics, DevOps, Backend",
    "19 patents — strong IP portfolio",
    "4 researchers with multiple affiliations — talent mobility risk"
  ],
  "keyRisks": [],
  "keyStrengths": [
    "Strong talent velocity — aggressive hiring signals",
    "Strong competitive position — deep IP and tech capabilities",
    "Executive stability — insiders holding/buying"
  ]
}

Output fields

| Field | Type | Description |
|---|---|---|
| entity | string | Company name passed to the tool |
| compositeScore | number | Weighted composite 0-100 (Velocity 25% + Inverted Drain 25% + Capability 30% + Inverted Flight 20%) |
| verdict | string | INVEST / MONITOR / CAUTION / AVOID |
| talentVelocity.score | number | Talent Velocity Score 0-100 |
| talentVelocity.totalOpenings | number | Total active job postings found |
| talentVelocity.seniorRoles | number | Count of senior/director/VP/principal roles |
| talentVelocity.techRoles | number | Count of engineering/developer/ML/AI roles |
| talentVelocity.growthSignal | string | CONTRACTING / STABLE / GROWING / SURGING / HYPERGROWTH |
| talentVelocity.signals | string[] | Human-readable signal narratives |
| brainDrain.score | number | Brain Drain Index 0-100 (higher = more drain) |
| brainDrain.inventorCount | number | Unique patent inventors found across USPTO and EPO |
| brainDrain.researcherCount | number | ORCID-registered researchers found |
| brainDrain.patentActivity | number | Total patents across both jurisdictions |
| brainDrain.drainLevel | string | RETAINING / STABLE / AT_RISK / DRAINING / HEMORRHAGING |
| brainDrain.signals | string[] | Signal narratives for patent decline, mobility, insider patterns |
| competitiveCapability.score | number | Competitive Capability score 0-100 |
| competitiveCapability.techDomains | string[] | Up to 8 detected technology domains ranked by signal frequency |
| competitiveCapability.patentStrength | number | Total patent count across USPTO + EPO |
| competitiveCapability.talentDepth | number | Public GitHub repository count |
| competitiveCapability.capabilityLevel | string | NASCENT / DEVELOPING / COMPETITIVE / LEADING / DOMINANT |
| executiveFlight.score | number | Executive Flight Risk 0-100 (higher = more risk) |
| executiveFlight.insiderSells | number | Total insider sell transactions found |
| executiveFlight.insiderBuys | number | Total insider buy transactions found |
| executiveFlight.sellRatio | number | Sell transactions divided by total transactions (0-1) |
| executiveFlight.riskLevel | string | LOW / MODERATE / ELEVATED / HIGH / CRITICAL |
| allSignals | string[] | Deduplicated list of all signal narratives across all models |
| keyRisks | string[] | Top risk narratives when any score breaches a threshold |
| keyStrengths | string[] | Top strength narratives when any score exceeds a positive threshold |

How much does it cost to run workforce intelligence queries?

This MCP server uses pay-per-event pricing — each tool call costs $0.045. Platform compute costs are included. You pay nothing while the server is idle.

| Scenario | Tool calls | Cost per call | Total cost |
|---|---|---|---|
| Quick test — one hiring signal check | 1 | $0.045 | $0.045 |
| Weekly competitor monitor — 4 tools | 4 | $0.045 | $0.18 |
| Full dossier on one target | 1 | $0.045 | $0.045 |
| Due diligence sprint — 10 companies | 10 | $0.045 | $0.45 |
| Portfolio monitoring — 50 companies/week | 50 | $0.045 | $2.25 |

You can set a maximum spending limit per run to control costs. The server stops charging when your budget is reached and returns a clear limit-reached message.

Compare this to dedicated talent intelligence platforms charging $15,000-80,000/year for comparable workforce signal data. Most users of this MCP spend $2-20/month with no subscription commitment.

Using the API directly

Python

import requests
import json
token = "YOUR_APIFY_TOKEN"

url = "https://workforce-competitive-intelligence-mcp.apify.actor/mcp"

headers = {

    "Content-Type": "application/json",

    "Authorization": f"Bearer {token}"

}
payload = {

    "jsonrpc": "2.0",

    "method": "tools/call",

    "params": {

        "name": "generate_workforce_dossier",

        "arguments": {"company": "Snowflake"}

    },

    "id": 1

}
response = requests.post(url, headers=headers, json=payload)

result = response.json()

content = json.loads(result["result"]["content"][0]["text"])
print(f"Company: {content['entity']}")

print(f"Verdict: {content['verdict']} (composite score: {content['compositeScore']})")

print(f"Growth signal: {content['talentVelocity']['growthSignal']}")

print(f"Drain level: {content['brainDrain']['drainLevel']}")

print(f"Capability: {content['competitiveCapability']['capabilityLevel']}")

print(f"Executive risk: {content['executiveFlight']['riskLevel']}")

for signal in content["allSignals"]:

    print(f"  - {signal}")

JavaScript

const token = "YOUR_APIFY_TOKEN";
const url = "https://workforce-competitive-intelligence-mcp.apify.actor/mcp";
const response = await fetch(url, {

  method: "POST",

  headers: {

    "Content-Type": "application/json",

    "Authorization": Bearer ${token}

  },

  body: JSON.stringify({

    jsonrpc: "2.0",

    method: "tools/call",

    params: {

      name: "benchmark_talent_strategy",

      arguments: { company_a: "Databricks", company_b: "Snowflake" }

    },

    id: 1

  })

});
const result = await response.json();

const content = JSON.parse(result.result.content[0].text);
const { comparison, talentAdvantage, capabilityAdvantage } = content;

console.log(Talent velocity advantage: ${talentAdvantage});

console.log(Capability advantage: ${capabilityAdvantage});
for (const [company, data] of Object.entries(comparison)) {

  const d = data as any;

  console.log(${company}: velocity ${d.talentVelocity.score} (${d.talentVelocity.growthSignal}), capability ${d.competitiveCapability.score} (${d.competitiveCapability.capabilityLevel}));

}

cURL

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