Naukri MCP Server

117-tool atomic MCP server for automating Naukri.com (India's largest job portal). Search jobs, apply in bulk, manage your profile, track applications, research companies, and monitor recruiter activity -- all from your MCP client. Designed for Claude Code's progressive Tool Search loading (default since Jan 2026), so each tool is single-purpose and discoverable on demand.

Tech stack: Python 3.10+, FastMCP, Playwright (persistent Chromium), aiohttp

Key capabilities:

• Search & Apply -- keyword search, personalized recommendations, single or batch apply with auto-answered screening questions
• Application Tracking -- local JSON persistence + 3-tier sync from Naukri's backend (REST, browser intercept, HTML scrape)
• Profile Management -- view/edit profile (naukri_get_profile, naukri_update_profile), boost visibility (naukri_boost_profile)
• Company Research -- naukri_research_company plus AmbitionBox bridge for salary data and employee reviews
• Performance Analytics -- naukri_search_impressions, naukri_recruiter_activity, naukri_activity_level
• Smart Automation -- naukri_auto_hunt (one-call job hunting with fit scoring), naukri_daily_brief (morning dashboard), naukri_tailor_resume, naukri_apply_top_fits (auto-apply to best matches)

---

Architecture

naukri.py                    # Entry point (FastMCP run)
naukri_server/
  __init__.py                # FastMCP setup + lifespan (browser start/stop)
  config.py                  # Constants, API endpoints, timeouts
  browser.py                 # PagePool (3 tabs) + TokenManager (JWT caching)
  api.py                     # Deduplicated _api_request, @api_tool decorator
  cache.py                   # Answer cache for auto-apply screening questions
  scoring.py                 # Alias-aware fit scoring
  validation.py              # Response validators (job lists, profiles, etc.)
  utils.py                   # Shared helpers
  tools/                     # 27 tool modules (117 tools)
    auth.py                  # Login, OTP verification, login status
    search.py                # Job search, recommendations
    jobs.py                  # Job detail, similar, compare, bulk, report fraud
    apply.py                 # Applications: list, detail, apply, batch, purge, stale, follow-up
    tracking.py              # Saved jobs: list, save, unsave, sync
    smart_apply.py           # Smart apply with fit scoring
    auto_hunt.py             # One-call automated job hunting
    profile.py               # Profile CRUD, dashboard, boost, audit
    resume_photo.py          # Resume/photo info, upload, download, delete
    resume_builder.py        # Resume templates, builder status, tailor
    sync.py                  # Sync applications/saved jobs, export
    insights.py              # Application insights, salary, match analytics, skill gap, taxonomy
    performance.py           # Search impressions, recruiter activity
    companies.py             # Company search, jobs, slug, research, follow/unfollow
    ambitionbox.py           # Salary data, reviews, interviews (AmbitionBox)
    inbox.py                 # Recruiter messages, NVites, mark_interested
    notifications.py         # Notification feed, mark read, count, summary
    settings.py              # Account settings, blocked companies, email, visibility, subscription
    alerts.py                # Job alert CRUD
    early_access.py          # Pre-posted roles from top companies
    mock_interview.py        # AI mock interview topics, sessions, history
    reminders.py             # Follow-up reminders
    daily_brief.py           # Morning dashboard summary
    health.py                # Endpoint validation, browser pool, AmbitionBox checks
    debug/                   # Multi-action debug tool (16 actions)

Hybrid Browser + REST Strategy

Naukri's Akamai CDN blocks direct REST calls for several endpoints. The server uses a hybrid approach:

Strategy	Used By	Why
Direct REST API	naukri_apply(), naukri_get_profile(), naukri_get_recommendations, naukri_sync, most reads	Fast, no browser tab needed. Uses JWT token extracted from browser cookies.
Browser intercept	naukri_search_jobs, naukri_company_jobs(), naukri_jobs (fallback)	Search API returns 406 on direct REST. Browser navigates to the page and intercepts the XHR response.
Browser UI automation	naukri_login(method="google"), naukri_boost_profile(), naukri_update_profile(), naukri_update_alert(), naukri_delete_alert()	Requires clicking buttons, filling forms, handling SSO popups. Akamai blocks PUT/DELETE via REST.
AmbitionBox scraping	naukri_company_intel (salary, reviews, interviews)	Extracts __NEXT_DATA__ from server-rendered Next.js pages.

PagePool

The server maintains a pool of 3 browser tabs (configurable via NAUKRI_MAX_TABS). Tabs are checked out with a semaphore, auto-recovered if crashed, and returned after use. This allows concurrent operations like batch apply to run in parallel without opening excessive tabs.

TokenManager

JWT authentication token (nauk_at cookie) is extracted from the Playwright browser context and cached in memory. On 401 errors, a single-writer refresh lock prevents parallel refresh storms -- one request refreshes, others wait and reuse the result.

3-Tier Sync Fallback

naukri_sync_applications() tries three strategies in order:

1. REST API -- paginated GET to the history endpoint (fastest, most reliable)
2. Browser intercept -- navigate to the applied-jobs page and capture the XHR response
3. HTML scrape -- extract job cards from the server-rendered DOM using adaptive CSS selectors

---

Quick Start for AI Consumers

1.  naukri_auth_status()             # Check session
    naukri_login(method="google")              # Authenticate (Google SSO or email)
2.  naukri_daily_brief()                     # Morning dashboard: recommendations + analytics
3.  naukri_auto_hunt(keywords="...", location="...")  # One-call job hunt with fit scoring
4.  naukri_assess_fit(job_id=...)           # Pre-flight check before applying
    naukri_apply(job_id=...)   # Submit application
5.  naukri_compare_jobs(job_ids=[id1, id2, id3])  # Side-by-side with fit scores
6.  naukri_accept_nvite(nvite_job_id="...")  # Respond to recruiter NVites
7.  naukri_sync_applications()       # Pull latest from Naukri backend
    naukri_list_applications()       # Query local tracking
8.  naukri_research_company(keyword="...")  # Unified: Naukri + AmbitionBox data
    naukri_company_intel(company="slug", intel_type="interviews")  # Interview tips
9.  naukri_tailor_resume(job_id=...)  # Get tailoring suggestions
    naukri_update_profile(...)     # Apply them
10. naukri_download_resume(save_path="...")  # Download resume

Apply flow detail: If a job has screening questions, the first naukri_apply() call returns them. Pass answers back in the second call. Answer keys are fuzzy-matched -- "current ctc" matches "What is your current CTC?". Answers are cached in questions.json so you only answer each question type once.

---

Tools (117 atomic)

Almost every tool follows the single-purpose atomic pattern — one MCP tool per operation. Only naukri_company_intel and naukri_debug keep an action/intel_type parameter (see the "Dispatcher tools" subsection below for why). This catalog is designed for Claude Code's progressive Tool Search loading (default since Jan 2026), so a large number of focused tools costs no more than a few multi-purpose ones.

Auth

• naukri_login(method=...) — Google SSO or email/password
• naukri_verify_otp(otp) — Submit OTP after login
• naukri_auth_status() — Check session validity

Job Search & Discovery

• naukri_search_jobs — Keyword search with browser intercept
• naukri_get_recommendations — Personalized job recommendations
• naukri_get_job(job_id) — Full job details
• naukri_similar_jobs(job_id) — Find similar jobs
• naukri_compare_jobs(job_ids) — Side-by-side with fit scores
• naukri_bulk_fetch_jobs(job_ids) — Up to 20 jobs in one call
• naukri_job_detail_v1(job_id) — Walk-in info, contact details
• naukri_report_fraud(job_id, reason) — Report fraudulent listing
• naukri_auto_hunt — One-call automated job hunting with fit scoring

Apply & Track

• naukri_apply(job_id, set_reminder_days=...) — Single apply with auto-reminder
• naukri_batch_apply(keywords=...) — Bulk apply from search
• naukri_assess_fit(job_id, apply_if_fit=False) — Fit assessment (auto-apply optional)
• naukri_score_saved_jobs(min_fit_score=60) — Score all saved jobs
• naukri_apply_top_fits(min_fit_score=70, limit=10) — Score + auto-apply top matches
• naukri_list_applications(...) — Query local tracking
• naukri_get_application(job_id) — Detailed application status
• naukri_purge_applications(before_date) — Delete old records
• naukri_stale_applications(...) — Detect stale applications
• naukri_follow_up_priority(...) — Cross-reference inbox + reminders
• naukri_draft_follow_up(job_id) — Generate follow-up message
• naukri_recruiter_history() — Per-company communication history

Sync & Export

• naukri_sync_applications(force_browser=False, days_back=365) — 3-tier sync
• naukri_sync_saved(force_browser=False) — Sync saved jobs
• naukri_export_data(data_type, export_format="json") — Export to JSON/CSV

Saved Jobs

• naukri_list_saved_jobs(limit=50, page=1) — List saved/bookmarked jobs
• naukri_save_job(job_id, ...) — Save a job
• naukri_unsave_job(job_id) — Remove a saved job
• naukri_sync_saved_jobs() — Pull from Naukri server

Inbox (recruiter messages)

• naukri_list_inbox(limit=20, unread_only=False) — List messages
• naukri_read_message(message_id, vcard_id, unique_id) — Read full message
• naukri_mark_interested(mail_id, conversation_id, interested=True) — Signal interest
• naukri_accept_nvite(nvite_job_id, ...) — Apply via NVite

Notifications

• naukri_list_notifications(limit=20, page=1, notif_type=None) — Filtered list
• naukri_notification_count() — Unread count
• naukri_mark_notification_read(notification_id, date) — Mark single
• naukri_mark_all_notifications_read() — Mark all
• naukri_notification_summary() — Unified dashboard

Profile

• naukri_get_profile() — Full profile
• naukri_update_profile(fields, ...) — Update profile fields
• naukri_audit_profile() — Completeness + tips
• naukri_boost_profile(randomize=False) — Re-save headline for visibility
• naukri_dashboard() — Profile dashboard data
• naukri_profile_targeting() — DFP targeting view

Resume & Photo

• naukri_resume_info() — Resume metadata
• naukri_upload_resume(file_path) — Upload PDF/DOC/DOCX
• naukri_download_resume(save_path) — Download to local file
• naukri_photo_info() — Photo metadata
• naukri_upload_photo(file_path) — Upload PNG/JPG/JPEG/GIF
• naukri_delete_photo() — Remove profile photo

Insights & Analytics

• naukri_application_insights(days=30) — Status breakdown + velocity
• naukri_salary_position(designation=...) — Salary positioning
• naukri_cached_answers(action="list|update|delete", key=..., new_answer=...) — Manage cached answers
• naukri_match_analytics(days=30) — Match-score per-field breakdowns
• naukri_match_quality(days=30) — Aggregate match quality
• naukri_skill_gap(...) — Skill gap vs market demand
• naukri_salary_benchmark(keywords, ...) — Market salary benchmark
• naukri_taxonomy() — Naukri's role taxonomy (37 dept × 167 categories × 1461 roles)
• naukri_profile_prompts() — Pending profile-completion actions
• naukri_conversion_funnel(days=30) — Application-to-interview funnel
• naukri_status_changes(days=30) — Detect status transitions

Performance

• naukri_search_impressions(days=7) — Search appearance stats
• naukri_recruiter_activity(page=1, limit=100, filter_by=None) — Recruiter actions on profile
• naukri_activity_level() — Current profile activity level

Companies

• naukri_search_companies(keyword, page=1, limit=10) — Find companies
• naukri_company_jobs(group_id, ...) — Jobs at a company
• naukri_company_slug(group_id) — AmbitionBox slug (single or batch comma-separated)
• naukri_research_company(keyword, ...) — Naukri + AmbitionBox combined
• naukri_follow_company(group_id|group_ids, action="follow|unfollow") — Follow/unfollow
• naukri_follow_status(group_id|group_ids) — Check follow status
• naukri_company_intel(company, intel_type="salary|reviews|interviews") — AmbitionBox intel

Settings

• naukri_get_settings() — All current account settings (job-search status, notifications, consent flags)
• naukri_update_settings(...) — Modify settings (pass only fields to change)
• naukri_blocked_companies() — List blocked companies
• naukri_check_email() — Email/mobile verification status
• naukri_visibility() — Resdex visibility toggles
• naukri_notification_prefs() — Email/SMS/push/WhatsApp preferences
• naukri_subscription_status() — Naukri 360 subscription + features

Job Alerts

• naukri_list_alerts() — All your saved-search job alerts
• naukri_alert_detail(alert_id) — Single alert details
• naukri_create_alert(name, keywords, ...) — Create new alert
• naukri_update_alert(alert_id, ...) — Edit alert fields
• naukri_delete_alert(alert_id) — Delete an alert

Early Access (pre-posted roles)

• naukri_list_early_access(...) — Browse pre-posted roles from top companies
• naukri_share_early_access(job_id) — Express interest (instant, no screening)

Resume Builder

• naukri_resume_templates() — Available templates (free + pro)
• naukri_resume_builder_status() — AI rewrite attempts left, subscription tier
• naukri_tailor_resume(job_id, ...) — Tailoring suggestions for a specific job

Mock Interview (AI)

• naukri_mock_interview_topics() — Available topics + completion status
• naukri_mock_interview_history() — Past interviews with scores/feedback
• naukri_start_mock_interview(job_id) — Start a JD-based mock interview
• naukri_answer_mock_interview(test_id, topic_id, question_id, answer) — Submit answer
• naukri_mock_interview_prep(job_id) — Interview prep bundle

Autonomous Agent

• naukri_agent_status() — Agent state + last 5 runs + config summary
• naukri_agent_config() — Full configuration
• naukri_agent_update_config(updates) — Patch config with JSON
• naukri_agent_run_now(ctx=None) — Execute one observe→decide→act→learn cycle
• naukri_agent_approve(cycle_id) — Apply pending decisions
• naukri_agent_reject(cycle_id) — Reject pending decisions
• naukri_agent_history(limit=10) — Recent run history
• naukri_agent_decisions(cycle_id) — Per-job decisions for one cycle

Background Scheduler

• naukri_scheduler_status() — Scheduler state + per-task last-run info
• naukri_enable_task(task_name) — Enable a disabled task
• naukri_disable_task(task_name) — Disable a task
• naukri_run_task_now(task_name) — Execute a task immediately
• naukri_task_history(task_name=None, limit=20) — Recent run history

Reminders & Interviews

• naukri_list_reminders(include_past=True, include_app_status=True) — All reminders with due status
• naukri_set_reminder(job_id, days=7, ...) — Create/update reminder
• naukri_interview_prep(job_id) — Interview prep package
• naukri_add_interview_round(job_id, round_type, ...) — Track interview round
• naukri_list_interview_rounds(job_id=None) — List rounds
• naukri_compare_offers(job_ids) — Compare multiple job offers

Dispatcher tools (only 2 left — kept by design)

• naukri_company_intel(company, intel_type="salary|reviews|interviews") — Three actions share the same company resolution + AmbitionBox auth flow; splitting would duplicate that orchestration.
• naukri_debug(action=...) — 16 dev-only debug actions across browser/API/discovery; catalog cost is real here even with progressive loading since most users never invoke them.

Other

• naukri_daily_brief — Morning dashboard: 16 sources + recommended actions
• naukri_health_check — Endpoint validation + browser pool + AmbitionBox

---

Setup

Prerequisites

• Python 3.10+
• Playwright Chromium (installed via playwright install chromium)

Installation

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
pip install -e ../jobcore     # shared scoring engine - see below
playwright install chromium

The jobcore dependency

The skill taxonomy, fit scoring and salary parsing live in a sibling package, jobcore; naukri_server/scoring.py and the domain/ scoring modules are thin re-export shims over it. It is not on PyPI, so it is installed one of two ways, and the two are deliberately kept apart:

where	how	why
local dev	pip install -e ../jobcore	edit jobcore and naukri together, no reinstall
CI	requirements-ci.txt, pinned to an exact commit	the runner has no ../jobcore checkout

Do not add the git URL to requirements.txt. It clobbers the editable install: after pip install -e ../jobcore, a later pip install -r requirements.txt uninstalls the editable package and replaces it with a git checkout - silently, since pip prints no "already satisfied" line for a direct-URL requirement. Measured on a clean venv 2026-08-20 and reproduced twice. Local iteration matters more than CI convenience here, so CI is the side that installs from git.

If you rebuild the venv, or see ModuleNotFoundError: jobcore, re-run pip install -e ../jobcore from this directory.

Bumping the pin in requirements-ci.txt is how a jobcore change is adopted - deliberately a visible, reviewable commit rather than a moving @master that could turn this repo's CI red with no change here.

First Login

Start the server:

python naukri.py

Then call naukri_login(method="google") from your MCP client. It opens a visible Chromium window where you can:

1. Google SSO (recommended): Click "Login with Google" -- uses the Chrome profile's saved Google session, no credentials needed.
2. Email/password: Pass method="email", email="...", password="...".

The browser session is stored in chrome-profile/ (auto-created, gitignored). This directory is machine-specific -- it contains cookies, local storage, and cached credentials. Do not copy it between machines.

Session Lifetime

Sessions persist for approximately 30 days. When expired, the server detects it at startup or on the first API call and returns a "Not logged in" error. Re-authenticate with naukri_login(method="google").

MCP Client Config

{
  "mcpServers": {
    "naukri": {
      "command": "python",
      "args": ["naukri.py"],
      "cwd": "/path/to/mcp-servers/naukri"
    }
  }
}

Environment Variables

All optional. Set in shell or a .env file.

Variable	Default	Description
NAUKRI_NAV_TIMEOUT	20000	Playwright page navigation timeout (ms)
NAUKRI_ELEMENT_TIMEOUT	5000	Playwright element wait timeout (ms)
NAUKRI_API_TIMEOUT	30	aiohttp REST API timeout (seconds)
NAUKRI_MAX_TABS	3	Max concurrent browser tabs in the PagePool

Data File Locations

All data files live in the project root and are gitignored.

File	Purpose
chrome-profile/	Playwright persistent browser profile. Machine-specific, never commit.
applications.json	Local application tracking. Written by apply, batch_apply, and sync.
saved_jobs.json	Local saved/bookmarked jobs. Written by saved_jobs and sync.
questions.json	Screening question answer cache. Auto-populated on apply, used by batch apply for auto-answering.
*.backup	Automatic backup before any JSON file overwrite (atomic write: write to .tmp, backup existing, rename).

---

Resilience Features

• Global aiohttp session -- single shared session for all REST calls, avoids connection overhead
• Deduplicated API layer -- _api_request with @api_tool decorator normalizes all REST interactions
• Refresh lock -- single-writer JWT refresh prevents parallel 401 storms
• Startup validation -- browser and token state validated before accepting tool calls
• Batch apply cancellation safety -- partial progress preserved if batch is interrupted
• Data backup -- .backup files created before every JSON overwrite
• Cache TTL auto-purge -- stale answer cache entries expire automatically
• Atomic writes -- sync state written via temp file + rename to avoid corruption
• Profile TTL cache -- profile data cached for 30 seconds to reduce redundant API calls

---

Known Limitations

Akamai CDN Blocks

Naukri uses Akamai Bot Manager. Several endpoints return 406 Not Acceptable or 403 Forbidden when called directly via REST without a browser session:

• Search (naukri_search_jobs) -- always uses browser intercept; direct REST is blocked
• Profile mutations (naukri_update_profile()) -- PUT/DELETE blocked by Akamai; browser automation used instead
• Job alerts -- CRUD operations go through browser UI automation for the same reason

This is expected behavior. Tools that require browser interaction are documented as such. If you see 406 errors from tools that should use REST, check login status with naukri_auth_status() -- an expired token causes Akamai to classify requests as bot traffic.

AmbitionBox Scraping

AmbitionBox is a Next.js SSR site. Salary and review tools extract __NEXT_DATA__ from server-rendered pages. If AmbitionBox changes their page structure, these tools may return errors. naukri_health_check includes an AmbitionBox check -- a "warn" status there is expected periodically and not a blocker for core Naukri functionality.

---

Troubleshooting

Problem	Solution
"Not logged in" errors	Session expired (~30 days). Call naukri_login(method="google") to re-authenticate.
Search returns empty / 406	Expected for direct REST. naukri_search_jobs uses browser intercept and should work. If it fails, run naukri_health_check.
Timeouts on slow connections	Increase NAUKRI_NAV_TIMEOUT (e.g., 30000) and NAUKRI_API_TIMEOUT (e.g., 60).
Rate limits / daily apply cap	Naukri limits daily applications by account type. The daily_applied field in apply responses shows your count. Naukri 360 subscribers get higher limits.
Browser tab crashes	The PagePool auto-recovers crashed tabs on the next acquire(). If persistent, restart the server.
Token refresh loops	Delete chrome-profile/ and re-authenticate from scratch.
naukri_sync fails all 3 tiers	Usually means an invalid session. Log in first. If already logged in, pass force_browser=True to skip the REST tier.
AmbitionBox salary/reviews broken	Run naukri_health_check to confirm. If AmbitionBox returns "warn", core Naukri tools are unaffected.

Health Check

Run naukri_health_check() to validate all integrations at once. It tests login session, profile API, search API (406 is normal here), recommendations, dashboard, browser pool liveness, and AmbitionBox scraping.

Returns {summary: {ok: N, warn: N, fail: N}, checks: [...]} with per-check timing.

---

Remote Access

Run the server on your always-on machine and connect from anywhere (web Claude in cowork environments, mobile, etc.). Two auth modes are supported and can run side-by-side on the same server.

Quick decision

Client	Auth mode	Why
Claude Code CLI	Bearer (MCP_SHARED_SECRET)	claude mcp add --transport http ... --header "Authorization: Bearer ..." works directly
Claude Desktop	Bearer (MCP_SHARED_SECRET)	Supports headers config in claude_desktop_config.json
Claude.ai web	OAuth (MCP_OAUTH_ENABLED=1)	Web UI only exposes OAuth client_id/secret fields, not bearer
Both at once	Bearer + OAuth (set both env vars)	Single server, OAuth provider's load_access_token falls back to the shared secret

Step 1 — Generate secrets

# Bearer secret (for Claude Code / Desktop)
python -c "import secrets; print(secrets.token_urlsafe(48))"

# OAuth client_id + client_secret (for Claude.ai web)
python -c "import secrets; print('client_id=claude-ai-web')"
python -c "import secrets; print('client_secret=' + secrets.token_urlsafe(48))"

Step 2 — Configure .env

Copy .env.example to .env and fill in. The .env file is gitignored. Minimal config to enable BOTH auth modes:

MCP_REMOTE=1
MCP_PORT=8321
MCP_PUBLIC_URL=https://naukri.<your-domain>

# Bearer (Claude Code + Desktop)
MCP_SHARED_SECRET=<paste output from token_urlsafe(48)>

# OAuth (claude.ai web)
MCP_OAUTH_ENABLED=1
MCP_OAUTH_CLIENT_ID=claude-ai-web
MCP_OAUTH_CLIENT_SECRET=<paste output from token_urlsafe(48)>
MCP_OAUTH_AUTO_APPROVE=1

If MCP_REMOTE=1 but no auth env var is set, the server refuses to start — this is the safety check that prevents accidentally exposing an unauthenticated MCP to the internet.

Step 3 — Public hostname (Cloudflare Tunnel recommended)

Cloudflare Tunnel gives you a stable public HTTPS URL without opening firewall ports. Free tier, unmetered bandwidth.

winget install Cloudflare.cloudflared
cloudflared tunnel login
cloudflared tunnel create naukri-mcp
cloudflared tunnel route dns naukri-mcp naukri.<your-domain>

Edit %USERPROFILE%\.cloudflared\config.yml:

tunnel: <UUID-from-create-command>
credentials-file: C:\Users\<you>\.cloudflared\<UUID>.json
ingress:
  - hostname: naukri.<your-domain>
    service: http://localhost:8321
  - service: http_status:404

Run the tunnel: cloudflared tunnel run naukri-mcp (or cloudflared service install for autostart).

Alternatives: Tailscale Funnel (peer-to-peer, lower latency for trusted devices) or ngrok (simpler but free tier has limits).

Step 4 — Start the server

# Load env vars from .env (PowerShell — use a one-liner or a helper script)
Get-Content .env | Where-Object { $_ -match '^[A-Z_]+=.+' } | ForEach-Object {
    $name, $val = $_ -split '=', 2
    [Environment]::SetEnvironmentVariable($name, $val, "Process")
}

python naukri.py --http

Logs should show Auth: OAuth provider enabled (issuer=https://naukri.<your-domain>, bearer-fallback=yes) and HTTP mode: 0.0.0.0:8321.

Step 5 — Connect clients

Claude Code CLI (uses bearer):

claude mcp add --transport http naukri https://naukri.<your-domain>/mcp `
  --header "Authorization: Bearer <MCP_SHARED_SECRET>"

Claude Desktop (uses bearer):

In claude_desktop_config.json:

{
  "mcpServers": {
    "naukri": {
      "url": "https://naukri.<your-domain>/mcp",
      "transport": "http",
      "headers": { "Authorization": "Bearer <MCP_SHARED_SECRET>" }
    }
  }
}

Claude.ai web (uses OAuth):

Settings → Connectors → Add custom connector

• URL: https://naukri.<your-domain>/mcp
• OAuth Client ID: claude-ai-web (matching MCP_OAUTH_CLIENT_ID)
• OAuth Client Secret: paste MCP_OAUTH_CLIENT_SECRET

Claude.ai will discover OAuth metadata automatically (FastMCP serves .well-known/oauth-authorization-server and the /authorize + /token endpoints).

Smoke test (curl)

# 401 expected — no auth header
curl -i https://naukri.<your-domain>/mcp

# Bearer flow — should return MCP JSON-RPC instead of 401
curl -i -H "Authorization: Bearer <MCP_SHARED_SECRET>" `
  https://naukri.<your-domain>/mcp

# OAuth metadata discovery
curl https://naukri.<your-domain>/.well-known/oauth-authorization-server | jq .

Windows host hardening

The MCP needs a headed Chrome session, so the host machine must stay awake and logged in.

# Disable sleep / hibernate while plugged in
powercfg /change standby-timeout-ac 0
powercfg /change hibernate-timeout-ac 0
# Disable screen-off (optional — Chrome stays alive when display sleeps,
# but this avoids GPU pauses)
powercfg /change monitor-timeout-ac 0

Behavior	Result
Lock screen	Chrome stays alive, MCP works
Logout	Chrome dies, MCP fails — keep the user session active
RDP disconnect	Process keeps running on host, MCP works
System sleep	Chrome resumes but in-flight calls fail — disable sleep
Manual Chrome use	Chrome on Windows can't run two instances with different --user-data-dir. Don't open the same profile manually while the MCP is running.

Monitoring

Cloudflare's "tunnel healthy" status only reflects the edge↔cloudflared link, not the origin. Add an external uptime probe (e.g., UptimeRobot, free) hitting https://naukri.<your-domain>/.well-known/oauth-authorization-server (200 expected) so you get notified when the host machine is actually unreachable.

Auth mode reference

Env var	Required for	Notes
MCP_REMOTE=1	Public bind	Without this, server stays on 127.0.0.1
MCP_PORT	Custom port	Default 8321
MCP_PUBLIC_URL	OAuth issuer / RS metadata	Defaults to http://localhost:8321
MCP_SHARED_SECRET	Bearer auth	>=32 chars; rotate by changing env + restart
MCP_OAUTH_ENABLED=1	OAuth flow	Enables /authorize, /token, /register, /revoke
MCP_OAUTH_CLIENT_ID	OAuth	Pre-registered client id for claude.ai
MCP_OAUTH_CLIENT_SECRET	OAuth	>=32 chars
MCP_OAUTH_AUTO_APPROVE	OAuth UX	1 skips consent screen (default), 0 shows Approve/Deny page at /oauth/consent