MCP Hub
Back to servers

nuke-mcp-server

The automotive knowledge graph. Fully normalized vehicle ontology at component resolution with provenance tracking.

npm313/wk
Updated
Mar 20, 2026

Quick Install

npx -y nuke-mcp-server

Nuke MCP Server

Vehicle intelligence for AI agents. Search 1.29M+ vehicle profiles, get comparable sales, AI valuations, $0/image analysis, and listing extraction.

npx -y @sss97133/nuke-mcp-server

What it does

Nuke is a vehicle data platform with 1.29M profiles, 1.36M observations, 11.7M auction comments, and 35M images aggregated from Bring a Trailer, Cars & Bids, RM Sotheby's, Mecum, Barrett-Jackson, eBay Motors, Craigslist, forums, and 100+ more sources.

This MCP server gives any AI agent access to that data through 11 tools:

ToolDescription
search_vehiclesQuick search by VIN, URL, year, make/model, or free text
search_vehicles_apiFull-text search with filters (make, model, year, price), pagination, and inline valuations
extract_listingExtract structured data from any car listing URL
get_vehicle_valuationCompute multi-signal market valuation ("The Nuke Estimate")
get_valuationLook up cached valuation by vehicle_id or VIN
identify_vehicle_imageAI vision: photo -> year/make/model/trim with confidence
analyze_imageYONO vision: make, condition, zone, damage, mods ($0/image)
get_compsComparable auction sales with price statistics
get_vehicleFetch a vehicle profile by ID
list_vehiclesList vehicles with filters (make, model, year, price range)
ingest_marketplace_listingSubmit pre-extracted FB Marketplace data directly to Nuke

Setup

1. Get an API key

Sign up at nuke.ag and generate an API key in Settings.

2. Configure your MCP client

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "nuke": {
      "command": "npx",
      "args": ["-y", "@sss97133/nuke-mcp-server"],
      "env": {
        "NUKE_API_KEY": "nk_live_your_key_here"
      }
    }
  }
}

Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "nuke": {
      "command": "npx",
      "args": ["-y", "@sss97133/nuke-mcp-server"],
      "env": {
        "NUKE_API_KEY": "nk_live_your_key_here"
      }
    }
  }
}

Cursor

Add to Cursor Settings -> MCP Servers:

{
  "nuke": {
    "command": "npx",
    "args": ["-y", "@sss97133/nuke-mcp-server"],
    "env": {
      "NUKE_API_KEY": "nk_live_your_key_here"
    }
  }
}

Example prompts

Once configured, you can ask your AI agent:

  • "What's a 1973 Porsche 911 worth right now?"
  • "Find comparable sales for a 1967 Ford Mustang Fastback"
  • "Extract the vehicle data from this BaT listing: https://bringatrailer.com/listing/..."
  • "What car is in this photo?" (with image URL)
  • "Analyze this car photo for condition and damage" (with image URL)
  • "Search for all BMW M3 E30s in the database"
  • "Look up VIN WP0AB2966NS420176"

Tools in detail

search_vehicles

Quick search. Accepts any input and auto-detects query type.

Input:  { query: "1967 Mustang", limit: 5 }
Output: { results: [...], query_type: "text", total_count: 142 }

Query types auto-detected:

  • VIN (17 chars) -> direct vehicle lookup
  • URL -> routes to extraction
  • Year (4 digits) -> vehicles from that year
  • Text -> multi-entity search across vehicles, orgs, users

search_vehicles_api

Full-text search with filters, pagination, sort, and inline valuations.

Input:  { q: "Porsche 911", make: "Porsche", year_from: 1990, year_to: 2000, limit: 50 }
Output: { data: [...], pagination: { page: 1, total_count: 312 }, search_time_ms: 45 }

Each result includes valuation with estimated_value and confidence_score when available.

extract_listing

Works on any car listing URL. No configuration needed.

Input:  { url: "https://bringatrailer.com/listing/1988-porsche-911-turbo-68" }
Output: { year: 1988, make: "Porsche", model: "911", trim: "Turbo", vin: "...", ... }

Supported sources include: Bring a Trailer, Cars & Bids, Hagerty, PCarMarket, RM Sotheby's, Mecum, Gooding, Bonhams, eBay Motors, Craigslist, Facebook Marketplace, Collecting Cars, and any generic listing page.

get_vehicle_valuation

Computes the Nuke Estimate using 8 signals:

  1. Comparable sales (recency-weighted)
  2. Condition assessment
  3. Rarity (production data + survival rates)
  4. Sentiment (auction comment analysis)
  5. Bid curve velocity
  6. Market trend (30d/90d)
  7. Originality score
  8. Record proximity
Input:  { vehicle_id: "uuid" }
Output: { estimated_value: 47500, value_low: 41000, value_high: 54000,
          confidence_score: 0.82, deal_score: "Good Deal", heat_score: 0.7,
          price_tier: "mainstream" }

get_valuation

Fast cached valuation lookup (no recomputation).

Input:  { vehicle_id: "uuid" }  or  { vin: "WP0AB2966NS420176" }
Output: { data: { estimated_value: 47500, confidence_score: 0.82, deal_score: "buy", ... } }

identify_vehicle_image

Tiered AI approach: Gemini Flash -> GPT-4o-mini -> GPT-4o for cost efficiency.

Input:  { image_url: "https://example.com/car.jpg" }
Output: { year: 1973, make: "Porsche", model: "911", trim: "Carrera RS",
          body_style: "coupe", generation: "G-Series", confidence: 0.87,
          reasoning: "Distinctive ducktail spoiler, Carrera side script..." }

analyze_image

YONO-powered deep analysis. $0/image (local inference, zero cloud API calls).

Input:  { image_url: "https://example.com/car.jpg", include_comps: true }
Output: { make: "Porsche", confidence: 0.91, family: "german",
          vehicle_zone: "ext_front_driver", condition_score: 4,
          damage_flags: [], modification_flags: ["aftermarket_wheels"],
          photo_quality: 4, source: "yono", cost_usd: 0,
          comps: [...] }

get_comps

Comparable vehicle sales from real auctions.

Input:  { make: "Porsche", model: "911", year: 1973, limit: 10 }
Output: { data: [...], summary: { count: 10, avg_price: 142000, median_price: 135000,
          min_price: 89000, max_price: 245000, auction_event_count: 8 } }

Data from: Bring a Trailer, Mecum, Barrett-Jackson, RM Sotheby's, Cars & Bids, Gooding, Bonhams, PCarMarket, eBay Motors, and more.

ingest_marketplace_listing

Submit pre-extracted Facebook Marketplace vehicle data directly to Nuke. Use this when you've already scraped data from a FB Marketplace listing page (e.g., via DOM extraction in a browser). Skips re-scraping — data goes straight to the database. Idempotent on facebook_id.

Input:  { facebook_id: "1234567890", title: "1984 Chevrolet K10",
          price: 18500, parsed_year: 1984, parsed_make: "Chevrolet",
          parsed_model: "K10", location: "Austin, TX",
          mileage: 87000, all_images: ["https://..."] }
Output: { success: true, listing_id: "uuid", vehicle_id: "uuid" | null,
          is_new: true, submission_count: 1, linked_via: "vin" | "ymm_match" | null }

Automatic vehicle linking: if the description contains a VIN matching an existing vehicle, vehicle_id is set directly. If year/make/model + state match 1-3 existing vehicles, suggested_vehicle_id is set for human review.

get_vehicle / list_vehicles

Standard CRUD for vehicle profiles with full filter support.

Environment variables

VariableRequiredDescription
NUKE_API_KEYYes*Your Nuke API key (nk_live_...)
NUKE_SERVICE_ROLE_KEYNoSupabase service role key (internal/dev use)
NUKE_API_URLNoAPI base URL (defaults to production)

*Either NUKE_API_KEY or NUKE_SERVICE_ROLE_KEY is required.

Data

  • 1,290,000+ vehicle profiles
  • 1,360,000+ observations with full provenance
  • 35M+ images indexed
  • 11.7M+ auction comments analyzed
  • 4.1M+ bids tracked
  • 773K+ valuations at 6.3% MAPE
  • 3,987 businesses identified
  • 112+ data sources

All data points have provenance tracking and confidence scores. BaT has NO public API -- Nuke is the only way to access structured BaT data programmatically.

Built by

Solo founder + Claude. The entire platform -- 300+ edge functions, 1000+ database tables, React frontend, Elixir API, YONO vision model -- was built by one person with AI.

License

MIT

Reviews

No reviews yet

Sign in to write a review