Linkedin Mcp Analyzer

LinkedIn saved jobs → EROI scoring → structured reports → git-committed market intelligence

Automated pipeline that scrapes your LinkedIn saved jobs, scores them against your profile using a 6-dimension EROI model, and writes actionable reports. Anti-bot aware — uses human-like patterns to avoid detection.

Python 3.12+ MCP Tests License: MIT

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🔍 What problem does this solve?

LinkedIn's job recommendations are noisy. Of 71 saved jobs analyzed, only 6% were relevant (SLEDOVAT/follow). The rest are noise — AI hype roles, fake-engineer titles, distant locations, non-strategic employers.

This tool replaces manual scrolling with a cache-aware, anti-bot pipeline:

📌 Your LinkedIn saved jobs (71 tracked)
   ↓
🔄 Skip-existing filter          ← 95% reduction (only new jobs scraped)
   ↓
🕷️ Sequential scraper (30s, no parallelism)  ← anti-bot — human rhythm
   ↓
📊 EROI scoring engine           ← 6 dimensions, YAML-configured weights
   ↓
📝 KB write-back                 ← metadata_stacku.json + agregovany_report.md
   ↓
📈 Synthetic market analysis     ← Frequency matrix + SNR + gap detection
   ↓
📦 Auto-committed to KB          ← git commit with full history

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✨ Features

Feature	What it does
Cache-aware scraping	Skips jobs already in KB (--skip-existing) — 95% time reduction
Anti-bot pattern	Sequential scraping (no parallelism), random delay 3-7s, fingerprint mix
Adaptive delay	Speeds up on success (0.95×), slows on errors (1.5×) — self-tuning
Session heartbeat	Refreshes LinkedIn auth every N jobs — prevents mid-run session expiry
3-layer YAML config	Source (max_pages) / Runtime (delay, timeout) / Analysis (N profiles)
Configurable EROI	--profile industrial switches thresholds + weights from YAML
Fingerprint mix	Random viewport, user-agent, locale, timezone per browser launch
6-dimension EROI scoring	Domain (35%), Tech (25%), Role (20%), Growth (10%), Formal (5%), Location (5%)
Fake-engineer detection	Identifies roles with "Engineer" in title but service/sales content
Synthetic report	Frequency matrix + SNR + gap detection + cluster analysis
Raw text cache	Stores raw job text in KB for re-scoring with different profiles
Git commit	Every pipeline run auto-commits to your knowledge base

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🚀 Quick Start

Prerequisites

• Python 3.12+
• uv (faster pip alternative — pip install uv)

Install

git clone https://github.com/outpost2026/linkedin-mcp-custom.git
cd linkedin-mcp-custom
uv sync

Authenticate (one-time)

.\linkedin-mcp.bat --login
# Opens a browser window — log into LinkedIn, then press Enter

Verify session

.\linkedin-mcp.bat --status
# ✅ Session valid  |  page.url = https://www.linkedin.com/feed/

Run the pipeline

# Full pipeline (scrapes only new jobs, skips existing KB entries)
.venv\Scripts\python scripts\run_pipeline.py --skip-existing

# Fast mode (reduced delays, no fingerprint — for quick tests)
.venv\Scripts\python scripts\run_pipeline.py --skip-existing --fast

# Partial run (first 15 jobs — for testing)
.venv\Scripts\python scripts\run_pipeline.py --limit 15

# Custom config + analysis profile
.venv\Scripts\python scripts\run_pipeline.py --config ~/.linkedin-mcp-custom/config.yaml --profile industrial

# Via MCP client
.\linkedin-mcp.bat
# Then in your MCP client: call analyze_saved_jobs

Output: agregovany_report.md + metadata_stacku.json + synteticky_report.md in your KB directory.

YAML Configuration

The pipeline uses a 3-layer YAML config (~/.linkedin-mcp-custom/config.yaml):

user: "default"
source:
  max_pages: 10                      # how many tracker pages to scan
runtime:
  headless: true
  delay_range: [3.0, 7.0]           # anti-bot delay between jobs
  page_timeout_ms: 30000
  session_heartbeat: 30              # refresh auth every N jobs
  fingerprint_mix: true              # random viewport/UA/locale
analysis:
  default:                           # baseline EROI profile
    thresholds: { sledovat: 65, medium: 50, hranicni: 40 }
    weights: { domain: 0.35, tech: 0.25, role: 0.20, growth: 0.10, formal: 0.05, location: 0.05 }
  industrial:                        # custom profile — switch via --profile industrial
    thresholds: { sledovat: 70, medium: 55, hranicni: 45 }
    weights: { domain: 0.50, tech: 0.20, role: 0.15, growth: 0.05, formal: 0.05, location: 0.05 }

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📊 Example output (from 71 real jobs)

PIPELINE REPORT
============================================================
Conclusion: ok
Duration: 142.31s total
Job IDs found: 71 (70 already in KB, 1 new)
Jobs scored: 1
Errors: 0

Verdicts: {'SLEDOVAT': 2, 'MEDIUM': 8, 'HRANICNI': 3, 'NESLEDOVAT': 2}

Full report: synteticky_report_analyza.md

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🧠 EROI Scoring Model

6 dimensions

Dimension	Weight	What it measures
Domain	35%	Industrial automation (core) vs adjacent vs noise
Tech	25%	Skill overlap — content-aware match ratio × coverage
Role	20%	Engineering role vs "fake engineer" (service/sales)
Growth	10%	Strategic employer (Siemens, ABB, Thermo Fisher…)
Formal	5%	Degree requirements with flexibility detection
Location	5%	Remote/hybrid/CZ vs distant/office-only

Thresholds

Score	Verdict
≥65%	🟢 SLEDOVAT (follow — apply now)
50–64%	🟡 MEDIUM (consider — mitigate gaps)
40–49%	🟡 HRANIČNÍ (borderline — only if time permits)
<40%	🔴 NESLEDOVAT (skip — no time allocation)

Special patterns detected

• Fake engineer: title says "Engineer" but content is service/sales → penalizes role
• Electronics manufacturing SMT/PCBA: caps domain score (adjacent, not core)
• Degree flexibility: "equivalent practical experience" found → adds ~5% to formal
• Positioning match: strong role match compensates for weak domain
• No-match penalty: tech score drops sharply when key skills missing

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🛠️ MCP Tools

Tool	Description
analyze_saved_jobs	Full pipeline: scrape → EROI score → KB write → git commit
get_saved_jobs	List all saved job IDs from LinkedIn tracker
get_job_details <id>	Full posting text for a single job ID
analyze_job <id>	EROI score a single job (avoids timeout)
check_session	Verify LinkedIn auth status with diagnostics

Timeout strategy: analyze_saved_jobs uses time-budgeted batch processing (default 45s). For full analysis, use the CLI pipeline or call analyze_job per job.

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📁 Output structure

B2B-Knowledge-Base/
└── 02_ANALYZY/
    └── 00_linkedin/
        ├── agregovany_report.md          # Human-readable EROI entries
        ├── metadata_stacku.json          # Machine-readable (schema v1.1)
        └── synteticky_report_analyza.md  # Market intelligence report

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🧪 Development

# Tests
.venv\Scripts\python -m pytest tests/ -v

# Lint
.venv\Scripts\python -m ruff check src/

# Type check
.venv\Scripts\python -m mypy src/

Debugging known issues

See the pitevni_kniha (autopsy book) for 28 documented bugs, root causes, fixes, and engineering rules.

Known issue	Status
MCP transport timeout for batch ops	✅ Fixed (time-budget + per-job tool)
Cookie lifecycle — silent expiry	✅ Fixed (session cache + checkpoint detection)
KB dedup fallback (industry=None)	✅ Fixed
Summary table non-idempotent	✅ Fixed
Pagination missing pages	✅ Fixed
CSS selector fragility	✅ Fixed
Refactor branch regression (34% → 100%)	✅ Fixed (new baseline branch)
Anti-bot vs. speed tradeoff	✅ Configurable (--fast, --profile)
Redundant scraping of known jobs	✅ Fixed (--skip-existing)

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🤝 Contributing

PRs welcome! This project especially needs:

• CI/CD pipeline — GitHub Actions for weekly scraping
• Docker deployment — containerize the MCP server
• More scorers — add dimensions (salary, benefits, team size)
• UI — simple dashboard for browsing scored jobs
• Translations — localize EROI labels for your market

Please read CONTRIBUTING.md first (coming soon).

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📄 License

MIT — see LICENSE.

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📈 Next iterations

Iterační backlog odvozený z 29 bug post-mortem záznamů a obscura-inspired pattern transferu. Každý návrh s hodnocením přínos/riziko.

#	Návrh	Přínos	Riziko	Doporučení
1	Optimalizovat page-turn na trackeru — zkrátit wait_for_timeout(17000) nebo najít specifický selector pro detekci načtení	↓40% času (ušetří ~60-80s)	Nízké — rychlejší navigace, stále sekvenční	✅ Teď — nejvyšší poměr přínos/riziko
2	Parallel per-job scraping — asyncio.gather s N kontexty	3-5× rychlejší (↓20-30s místo ~110s)	Vysoké — emergentní bot fingerprint (Z025) už jednou spadl z 98% na 34%	❌ Počkat na lepší anti-bot strategii
3	CI/CD weekly scrape — rozchodit GitHub Actions workflow	Plně automatický monitoring	Střední — cookie export (Z021) vyžaduje ruční refresh každých pár týdnů	⏳ Po stabilizaci skórovacího profilu
4	Synthetic report 2.0 — trend analýza, skill gap evoluce, časové řady	Vyšší vypovídací hodnota než statický snapshot	Nízké — jen nová analytická vrstva	⏳ Vyžaduje 2-3 historické snapshoty
5	Multi-portál (Jobs.cz, Profesia) — nový scraper per obscura worker pattern	Širší pokrytí trhu	Střední — předčasná abstrakce (obscura transfer sekce 8.3)	❌ Počkat na 100+ jobů v KB
6	Auth guard deduplication — parametr skip_auth_check v extract_page() pro pipeline režim	Eliminace redundantní navigace na feed	Nízké — čistě refaktor bez změny chování	✅ Teď — navazuje na fix Z029
7	Sbírat job IDs z API interceptu místo DOM/script scanningu (Z018, Z013)	Rychlejší + spolehlivější extrakce	Střední — API formát se může změnit	⏳ Až LinkedIn změní aktuální strukturu

Priority

Kdy	Co
Teď (1-2 běhy)	#1 page-turn optimalizace + #6 auth dedup
Brzy (3-5 běhů)	#3 CI/CD + #4 report 2.0
Až bude dost dat (100+)	#5 multi-portál
Až LinkedIn zlomí aktuální extrakci	#7 API intercept

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🧭 Why this exists

Built by a systems integration engineer who got tired of LinkedIn's noise-to-signal problem. The name "EROI" comes from energy-return-on-investment — a concept borrowed from off-grid solar (which the author also builds). The same principle applies to job hunting: don't spend energy where the return is negative.

"LinkedIn recommends everything. This tool tells you what matters."

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🔗 Links

• Synthetic market report (49 jobs)
• Aggregated report (49 entries)
• Bug autopsy book
• MCP practical workflow guide
• Shared MCP postmortems