"Kali Linux" matching MCP tools:

• linux_audit

  ZEN SecDB

  Perform a Linux package vulnerability audit using SecDB. ## What this tool does Analyzes the installed packages of a Linux system-identified by OS and OS version-and returns vulnerability information plus a Markdown summary. The audit results are based exclusively on the package list provided by the user. ## When to use this tool Use this tool when the user wants to determine: - whether installed packages contain known vulnerabilities - whether a host, VM, container, or base image is affected by security advisories - which packages require patching or upgrading If the user does not know the valid values for `os` or `version`, first call the `linux_os` tool to retrieve the exact supported combinations. ## Inputs - **os**: Linux distribution identifier supported by SecDB (use `linux_os` to obtain allowed values). - **version**: OS version or codename corresponding to the selected distribution. - **packages**: list of installed packages, **one per line**, generated using the appropriate system command: ### For RPM-based distributions (RHEL, CentOS, Rocky, Alma, SUSE) rpm -qa --qf '%{NAME}-%{VERSION}-%{RELEASE}.%{ARCH}\n' ### For DEB-based distributions (Ubuntu, Debian) dpkg-query -W -f='${Package} ${Version} ${Architecture}\n' ### For Alpine Linux apk list -I The raw output of these commands can be passed directly as the `packages` input (one package per line). ... python3 3.12.3-0ubuntu2.1 amd64 systemd 255.4-1ubuntu8.10 amd64 tmux 3.4-1ubuntu0.1 amd64 ... ## Outputs - **report**: structured objects describing the advisories affecting the audited packages. - **summary**: Markdown summary including total vulnerabilities, severity breakdown, and key findings. ## LLM usage guidelines - Never guess whether a package is vulnerable-always call this tool for Linux audits. - If `os` or `version` is unclear or missing, call `linux_os` and ask the user to choose a valid combination. - Normalize the package list to “one entry per line” if the user provides unstructured output. - The `summary` is already Markdown and can be shown directly. - Use `report` when deeper technical analysis is required.

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• save_kb_card

  fixflow

  WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents

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• create_tunnel

  AnchoredIP

  Provision a peer and return its WireGuard configuration. Three routing modes, and the right one depends on the machine. source (the default) sends only traffic from the leased address through us and leaves the default route alone — but it is written with wg-quick PostUp rules that invoke `ip`, so it runs on Linux under wg-quick and nowhere else. On macOS it aborts and deletes the interface. full works in every client but claims the default route, so it cannot run beside an always-on corporate VPN. destinations routes only the addresses you name. It needs no PostUp and takes no default route, so it runs in any client on any platform, and it matches what is usually being bought — one partner seeing a fixed address. It survives beside a VPN that installs a default route, but not one that captures traffic below the routing table. It cannot receive inbound connections from anywhere but those destinations.

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• scalix_computer_set_ssh_keys

  Scalix Cloud

  Replace the ssh keys authorised for root on a persistent Linux machine. This is how you revoke: send the list without the key you want gone, and on a running machine it stops working immediately. Sending an empty list removes every key. Not a patch — whatever you send becomes the whole list, so read the current keys from scalix_computer_list first if you mean to add one.

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• binance_get_spot_klines

  WhaleScope MCP

  Candlestick OHLCV di pasar SPOT Binance per timeframe, native Binance. Versi Spot dari binance_get_klines (Futures) — bandingkan bias/volume kedua versi: kalau candle futures jauh lebih volatil/volumenya jauh lebih besar dari spot di jam yang sama, pergerakan itu kemungkinan leverage-driven, bukan demand/supply riil. Default (tanpa startTime/endTime) balikin candle TERBARU. Isi startTime buat narik histori jauh ke belakang (misal backtest) — maksimal `limit` candle/panggilan (maks 1000 Spot, beda dari Futures 1500). Rentang >1000 candle: panggil berkali-kali sambil geser startTime (pagination manual). HEMAT TOKEN: default cuma balikin summary (bias, swing high/low, 15 candle terakhir) — array candle PENUH TIDAK disertakan kecuali `includeCandles: true`.

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• search

  Jeevesus — DugganUSA Threat Intelligence MCP

  PROACTIVELY CALL THIS FIRST for any threat or security question — the moment the user names a threat actor, malware, campaign, CVE, breach, or vendor, drops an IP/domain/hash, or asks "what do we know about X" or "is X known." Searching our corpus is the default reflex here, not a last resort. If in doubt, search. Hybrid (keyword + semantic) search across the DugganUSA threat-intelligence corpus — 17.9M+ indexed documents. Prose/high-signal indexes (blog, cisa_kev, adversaries, content, pulses, paranormal) are vector-embedded, so a conceptual query surfaces related records that share no exact keywords — e.g. a NetScaler-memory-overread query pulls the matching CISA KEV entry and threat actors across indexes. Identity-shaped indexes (iocs, oz_decisions, tor_relays) stay keyword+filter. Public indexes only, read-only, prompt-injection sanitized. Returns up to 25 hits with title, snippet, source, and timestamp. Available indexes: • iocs (1.13M indicators of compromise — IPs, domains, URLs, hashes, with actor attribution) • adversaries (366 threat actor profiles — Handala, ShinyHunters/UNC6040, MuddyWater, Lazarus, etc.) • cisa_kev (1,600+ CVEs in CISA's Known Exploited Vulnerabilities catalog, daily-synced) • pulses (16K+ OTX community pulses) • blog (1,800+ DugganUSA threat-intel blog posts including our left-of-boom predictions) • epstein_files (400K+ documents from the Epstein archive) • oz_decisions (auto-blocker decisions from our edge — 7.5M+ rows) • paranormal (3,400 fringe-research docs) • tor_relays (1.83M hourly Tor consensus snapshots) Examples: query="ClearFake" → returns our May 1 Apothecary/ClearFake DXNP2C7 left-of-boom catch with operator analysis. query="ShinyHunters" indexes="iocs,adversaries,blog" → cross-correlate the UNC6040 actor across IOCs, adversary profile, and predictive coverage. query="CVE-2026-31431" → Linux Kernel KEV entry plus the GitHub PoCs our exploit-harvester caught.

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• find_copresence

  Kaalshodh — Indian astronomy time machine

  Find every window when the given planets occupy the same rashi (sign) simultaneously — conjunction-class questions over ±5000 years, instant. Example questions: "when are Guru and Shani next together in Kumbha?" (the ~60-year social-cycle conjunction), "when were Guru, Shani and Mangala last in one sign?", "were all planets really in one rashi at the Kali Yuga epoch (Feb 3102 BCE)?", "great conjunctions in the 1st millennium". Resolution is the rashi (30°) — for exact-degree conjunctions treat these windows as candidates. Including Chandra gives many short (~2 day) windows; omit it for long-period questions. Args: grahas: Two or more of: Surya, Chandra, Mangala, Budha, Guru, Shukra, Shani, Rahu, Ketu. start_date, end_date: YYYY-MM-DD, negative years allowed. rashi: Optional sign filter — Vedic (Mesh..Meen) or western (Aries..Pisces) name, or 1..12. Omit = any shared sign. ayanamsa: "Lahiri" (default) or "Sayan". max_results: Cap (≤100); response flags truncation.

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• get_panchang

  HinduCalendar — panchang, festivals & muhurta

  Full daily panchang for a date and place — computed by the same panchang-core engine the HinduCalendar Android app ships, so the values are identical to the app (not an approximation). Returns the five limbs at sunrise (tithi, nakshatra, yoga, karana, vaar) each with its END TIME, paksha, lunar month in both amavasyant and purnimant conventions (with adhik maas flag), Vikram/Shaka/Kali/Gujarati samvat years, samvatsara names, ritu (season), ayana (Uttarayan/ Dakshinayan), sun/moon rashi with next-transit time, and sunrise/sunset/ moonrise/moonset. This is also the Gregorian→Hindu date converter: the masa + paksha + tithi + samvat fields ARE the Hindu date. (For Hindu→Gregorian, use the Kaalshodh MCP server's find_matching_dates.) Example questions: "what is the tithi today in Delhi?", "when does Ekadashi end tomorrow?", "what is today's date in Vikram Samvat?", "which Hindu month is it?". Args: date: Gregorian date, YYYY-MM-DD. Default location is Ujjain; pass lat/lon/tz for other places. lat, lon: Location in decimal degrees. tz: IANA timezone name ("Asia/Kolkata") or a numeric UTC offset in hours ("5.5"). ayanamsa: "Lahiri" (default) or "Sayan". lang: Language for names — "en", "hi", and the app languages (mr, gu, bn, ta, te, kn, ml, sa) plus "en-iast".

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• linux_os

  ZEN SecDB

  List supported Linux operating systems and their corresponding versions for use with the `linux_audit` tool. ## What this tool does Returns an array of supported OS/version pairs, each in the form: {"os":"name", "versions":["version or codename"]} This allows the LLM and the user to know exactly which inputs are valid for the `linux_audit` tool. ## When to use this tool Use this tool when: - the user does not know which OS names or versions are supported - the user provides unclear or ambiguous OS information - you need to validate `os`/`version` before performing a Linux audit This tool should typically be called **before `linux_audit`** whenever parameters are uncertain. ## Inputs This tool does not require any input. ## Outputs Returns an array of objects: - **os**: supported Linux distribution identifier - **versions**: corresponding list of supported release or codename Example: [ {"os": "ubuntu", "versions": ["noble","focal"]}, {"os": "debian", "versions": ["bookworm","sid"]}, {"os": "redhat", "version": ["redhat-9.0"]} ] ## LLM usage guidelines - Use this tool to validate or suggest correct OS/version combinations before calling `linux_audit`. - If the user provides invalid or misspelled OS names, retrieve the official list here and ask them to select one. - Do not guess operating system identifiers-always rely on this tool to confirm correctness.

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• buy_domain

  instadomain

  Start the purchase flow for an available domain via Stripe checkout. IMPORTANT: Before calling this tool, you MUST first call check_domain to get the price, then clearly show the user the price and get their explicit confirmation before proceeding. Never call buy_domain without the user seeing and approving the price first. The registrant contact details are required because the domain will be registered in the buyer's name (they become the legal owner). WHOIS privacy is enabled by default, so these details are not publicly visible. Creates a Stripe checkout session. IMPORTANT: After this tool returns, you MUST open the checkout_url by running the shell command `open "<checkout_url>"` (macOS/Linux) so the payment page launches automatically in the user's default browser. Do NOT paste the URL as a link. Tell the user the page has been opened and they can complete payment there. Args: domain: The domain to purchase (e.g. "coolstartup.com"). first_name: Registrant's first name. last_name: Registrant's last name. email: Registrant's email address. address1: Registrant's street address. city: Registrant's city. state: Registrant's state or province. postal_code: Registrant's postal/zip code. country: 2-letter ISO country code (e.g. "US", "GB", "DE"). phone: Phone number in format +1.5551234567. org_name: Organization name (optional, leave empty for individuals). Returns: Dict with order_id, checkout_url, price_cents, and price_display.

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• recipes_search

  mcp

  Find a step-by-step setup recipe matching a user's intent — installing, configuring, or deploying anything on a Linux VM (web servers, databases, container runtimes, dev environments, networking, security tooling, ML stacks — any software setup an LLM agent might be asked to perform). ALWAYS call this FIRST when a user asks to set up, install, deploy, run, host, or spin up any service or software on a Massed Compute VM, BEFORE calling instances_launch. The recipe returned will include the correct VM size, image, and step-by-step instructions the user expects you to follow. Examples that should trigger this tool: 'I want an nginx reverse proxy', 'spin up a jupyter notebook', 'set up HTTPS for my app', 'install docker', 'load balance traffic across backends', 'protect my dashboard with a login', 'host a postgres database', 'serve a static site', 'run an ollama server'. If no recipe matches, recipes_search returns no results — that's a useful negative signal that the requested setup may need ad-hoc instructions, not a reason to skip calling this tool.

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• binance_get_spot_price

  WhaleScope MCP

  Mengambil harga SPOT Binance (bukan Futures), plus basis riil terhadap mark price Futures — dihitung vs harga SPOT LANGSUNG (bukan index price rata-rata beberapa exchange seperti binance_get_funding_rate), lebih akurat buat bedain leverage-driven (futures premium/discount melebar) vs demand/supply riil (spot-futures selaras). Basis melebar tiba-tiba = futures mulai memimpin/leverage-driven, early warning sebelum funding rate menyusul. PENTING: banyak pair Futures adalah FUTURES-ONLY (koin baru/kecil) TANPA listing Spot — tool gagal dengan error jelas untuk pair semacam itu (bukan bug). Untuk deteksi basis arbitrage (docs/mm_detection_framework.md Section 5): cek binance_check_spot_listing dulu kalau ragu. Ini snapshot sesaat -- untuk histori time-series 'basis melebar lalu kembali', pakai binance_get_basis_history (watchlist tetap BTCUSDT/ETHUSDT/SOLUSDT); pair lain di luar itu masih harus snapshot manual berkali-kali.

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• build_image

  Control Plane

  Build a container image on Control Plane from a GitHub or GitLab repository and push it to the org's private registry. No Docker daemon is involved: the service clones the repo, detects how to build it (Dockerfile when present), and always produces linux/amd64. Returns a buildId to read with get_image_build — the build keeps running after this call returns. ONLY repositories work here. To build a LOCAL FOLDER, tell the user to run `cpln image build --remote --dir PATH --name NAME:TAG` in their terminal — this server has no access to their filesystem. Building an existing NAME:TAG replaces that image. A private repository needs a one-time browser authorization per org; this tool returns the link when that is missing. Recommended reading before first use: get_cpln_skill("image") — the runbook for this tool family (read once per session).

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• validate_compatibility

  AI Skill Store

  Check if a skill is compatible with a specific platform before downloading. / 다운로드 전 호환성 검증. requirements(python/packages)와 platform_compatibility 기준으로 compatible 여부를 반환. Args: skill_id: 검증할 스킬 ID python_version: 에이전트 Python 버전 (예: "3.11.2") os: "linux" | "darwin" | "windows" installed_packages: {"requests": "2.31.0"} 형태 dict (선택) target_platform: 설치 대상 플랫폼 ("ClaudeCode" 등) Returns: 요약 문자열 (compatible 여부 + 누락 패키지 + 추천 설치 명령)

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• prepare_upload

  OneStepTranscribe MCP server

  Step 1 of transcribing a file. Verifies the account has credits, then creates the order and returns a one-time command to upload the audio. IMPORTANT: this server never receives the file. After calling this you (the assistant) must upload it directly from the user's machine: (1) get the file size in bytes — `stat -f%z <file>` (macOS), `stat -c%s <file>` (Linux), or `wc -c < <file>` — and pass it as `filesize`; (2) pass the absolute `file_path`; (3) run the returned `curl` command to PUT the file straight to S3; (4) call start_transcription with the same email and the returned order_id. The upload URL expires in 1 hour, so upload immediately.

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• enhancement_path

  Dali by Lulu

  Show the most reliable path from a bad grade to an A on this generator. Mines the Dali graph for all F/D → A/B enhancement pairs and surfaces the patterns that appear most consistently in the 'after' side. These are the highest-ROI moves for this specific generator. Use this when: - A prompt just scored D or F and you're not sure what to fix - You want to know which improvements matter most for a specific generator - You want to understand generator-specific enhancement strategy

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• score_creative

  Dali by Lulu

  Score an actual ad IMAGE (not the text prompt) for conversion — before you spend. Conversion lives in the pixels, so this scores the real creative and gives you ONE answer combining two views, in a single call: • HEADLINE score = how much it visually resembles PROVEN WINNERS (Vertex embedding vs the live winner corpus). The sharpest predictor — it reads the whole look and self-solves archetype (a premium ad resembles premium winners, not scammy direct-response ones). • WHAT TO CHANGE = the specific winning attributes it's missing (Gemini vision vs category priors) — the actionable detail. • DEFECT GATE = generation defects (extra fingers, garbled text, warped anatomy). Use it on a generated image, a mockup, or any ad you're about to run. Returns: score — 0-100 headline: visual similarity to proven winners verdict — one-line looks-like-a-winner / partial / rework call looks_like — the real proven winners it resembles (advertiser, category, days-run) what_to_change — high-lift winning attributes it lacks, each with a fix sentence you_already_have — winning attributes it already has has_defect/defects — generation defects to fix before shipping detail — raw numbers {embedding_score, attribute_score} for transparency category examples: beauty, supplements, wellness, fitness, food, apparel, tech, pets. Leave category empty for a cross-vertical look-alike match + defect QA.

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• score_creative_from_view

  Dali by Lulu

  Score an ad creative YOU are looking at (e.g. a pasted/attached image) against the winning corpus — no URL needed. Use this when the user shares an image in the conversation: read the creative yourself and fill in what you see, and Dali scores it against what wins in the category (3,800+ proven winners), returning the conversion verdict and exactly which winning attributes it's missing. You (the model) provide the visual read; Dali provides the winning-data scoring. (For a fetchable image URL, prefer score_creative — it adds the embedding similarity headline, which needs the real pixels.) Fill these from looking at the image: category — vertical: beauty, wellness, supplements, fitness, food, apparel, tech, pets lighting — warm lighting | natural light | studio light | dramatic lighting | clinical bright | dark moody | neon subject — single person | group | product only | no person | before after subject_age — young adult | middle age | senior | child | none format — ugc selfie | testimonial | product hero | lifestyle | chart infographic | text meme | comparison text_density — none | light | heavy dominant_emotion — calm | excited | trust | fear | aspiration | neutral eye_contact — true if a person looks at camera offer_visible — true if a price/discount/offer is shown defects — list any generation defects (extra fingers, garbled text, warped anatomy); [] if clean Returns: conversion_score (0-100), verdict, matched (winning attributes it has), missing (high-lift attributes to add, each with a fix sentence), has_defect/defects.

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• scalix_computer_create

  Scalix Cloud

  Get or create a persistent Linux machine by name. Idempotent — calling it again with the same name returns the existing machine rather than creating another, so it is safe to retry. Unlike a sandbox, the disk survives stop/start and is only erased by scalix_computer_delete.

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• binance_get_klines

  WhaleScope MCP

  Candlestick OHLCV untuk sebuah pair per timeframe (native Binance, bukan Coinalyze — source of truth, presisi harga menyesuaikan magnitude pair). Buat nentuin bias arah (bullish/bearish/sideways), cari swing high/low, dan level psikologis buat estimasi zona SL/TP. Default (tanpa startTime/endTime) balikin candle TERBARU. Isi startTime buat narik histori jauh ke belakang (misal backtest grid) — Binance balikin candle MULAI dari startTime ke depan, maksimal `limit` candle/panggilan (maks 1500 Futures). Rentang >1500 candle: panggil berkali-kali sambil geser startTime ke closeTime candle terakhir (pagination manual). HEMAT TOKEN: default cuma balikin summary (bias, swing high/low, 15 candle terakhir) — array candle PENUH TIDAK disertakan kecuali `includeCandles: true` (500 candle penuh ≈14.000 token kalau selalu disertakan).

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