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linkedin-mcp-custom

by outpost2026
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🤖 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

🔍 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

Related MCP server: LinkedIn-Posts-Hunter-MCP-Server

✨ 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

🚀 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 }

📊 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

🧠 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

🛠️ 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.

📁 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

🧪 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)

🤝 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).

📄 License

MIT — see LICENSE.

📈 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

🧭 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."

🔗 Links

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–Response time
–Release cycle
–Releases (12mo)
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