# OpenDealer Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## OpenDealer

Search vehicles across dealerships, compare prices, check safety ratings and recalls, and get deal scores.

- Record: https://musedirectory.ai/connector/opendealer
- Category: Shopping & Commerce
- Developer: OpenDealer (https://opendealer.pro)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 1519ms, checked 2026-09-28T08:45:56Z
- Endpoint: https://mcp.opendealer.app/rpc
- Auth: Needs an access key; Pricing: unknown
- Screening: Screened, no issues found (2026-09-24T08:20:46Z)
- Source: Found in the official MCP Registry (app.opendealer/mcp) https://registry.modelcontextprotocol.io/v0/servers?search=app.opendealer%2Fmcp

Connects to OpenDealer's automotive inventory database covering dealerships, vehicles, pricing, and market data. Muse can search for vehicles by make/model/location, get detailed specs and safety information, check NHTSA recalls, compare prices across the market, and provide deal scoring to help with purchase decisions.

Example request: "Find me a used Honda Civic under $25,000 within 50 miles of my zip code and tell me if it's a good deal."

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use OpenDealer to help me. It is a free service with an MCP server at https://mcp.opendealer.app/rpc. It needs an API key from OpenDealer; ask me to enter it through your secure credential prompt. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- search_vehicles: Search for vehicles across dealerships (Meilisearch-backed NL + structured filters). Preferred tool order for assistants: 1. list_facets or list_research_makes/list_research_models to discover valid values 2. filter_vehicles (map shopper intent onto labeled hard filters) 3. get_v
- filter_vehicles: Preferred structured inventory lookup when make/model/year/color/location are known. Uses exact hard filters (keyword mode, no embeddings) via the Runtime /v1/llm/filter path. Resolve exact make/model names with list_research_makes and list_research_models first. Map shopper pros
- list_facets: Discover available filter values and counts (makes, body types, fuel types, price/year ranges) for the live inventory. Call this before filter_vehicles when you need valid dimension values. Optional make/near/radius scopes the facet counts.
- list_research_makes: Browse the research catalog of vehicle makes (with model and inventory counts). Use to resolve exact make names/slugs before filter_vehicles or research_model.
- list_research_models: List models for a make from the research catalog (MSRP/body summaries). Use before filter_vehicles or research_model when the model name is uncertain.
- get_vehicle: Get complete details for a specific vehicle by VIN. Returns comprehensive Schema.org Vehicle data including: • Full specifications (engine, transmission, drivetrain) • High-resolution images • Current pricing and availability • NHTSA NCAP safety rating summary (when available) • 
- check_recalls: Get NHTSA open safety recalls for a vehicle by VIN. Returns recall campaigns resolved at the year/make/model level (YMM-granular). A recall listed for the model year may not apply to every VIN — the response includes NHTSA's disclaimer and campaign details (component, summary, re
- get_dealer: Get comprehensive information about a specific dealership. Returns Google-enriched dealer knowledge optimized for assistants: • Name, address, phone, website • Google rating, review count, hours, business status • Inventory count and OpenDealer profile links • Contact points for 
- get_safety_rating: Get NHTSA 5-Star Safety Ratings for a year/make/model (no VIN required). Answers questions like "is a 2023 RAV4 safe for my family" with: • Overall and crash-test star ratings (when published) • Rollover rating / possibility • NHTSA-evaluated ADAS availability (ESC, FCW, LDW) Rat
- get_vehicle_history: Get OpenDealer listing history for a VIN: price changes, days on lot, and status. Answers "has this VIN dropped in price" and days-on-lot narratives from retained snapshots (including vehicles that left a dealer feed). Returns: • Chronological price history with per-snapshot chan
- dealers_near: Find dealerships near a location. Location modes (choose ONE): • zip + radius (miles) • lat + lng + radius • city + state + radius • county + state + radius Returns dealer information including: • Name, address, phone, website • Distance from search location • Current inventory c
- dealer_inventory: Browse the complete inventory of a specific dealership. IMPORTANT: Use the exact dealer slug from a previous dealers_near or search_vehicles response. Do NOT guess dealer IDs. Useful when a user wants to see what a particular dealer has in stock. Supports all vehicle filters (mak
- compare_vehicles: Compare 2-5 vehicles side by side. Returns a structured comparison including specs, price context, and per-vehicle cite fields (vin + shop url). CRITICAL: CITE: Each vehicle's cite object is `vin` + `url` (canonical opendealer.shop VDP). Cite only `url` to shoppers. Never invent 
- get_deal_score: Get AI-powered deal scoring and market insights for a vehicle. Returns comprehensive analysis including: • Deal score (1-100) with rating (Great, Good, Fair, Poor) • Price comparison vs market average • Days on lot analysis • Price history and trends • Similar vehicles in the mar
- get_market_overview: Get high-level automotive market statistics. Returns aggregated market data including: • Total vehicles and dealers in inventory • Average pricing by segment • Top makes by volume • Market velocity indicators • New vs Used breakdown
- get_market_segment: Get detailed pricing and market data for a specific vehicle segment. Useful for understanding fair market value for a make/model/year combination. Returns pricing statistics including: • Average, median, min, max prices • Price percentiles (10th, 25th, 75th, 90th) • Average milea
- list_market_segments: Browse market segments with pricing statistics (modelcode, median price, sample size). Use to discover modelcodes for get_market_segment / get_market_trends.
- get_market_trends: Price trends over time for a market segment (modelcode). Returns timeline of median/avg prices and days-on-lot.
- get_market_velocity: How quickly vehicles sell by segment (fastest/slowest days on lot). Optional make/type filters.
- compare_market: Compare pricing across market segments. Provide modelcodes[] or make (optionally with model).
- get_suggested_rates: National average suggested auto loan APRs (not a credit offer). Optional filters: condition (new/used), term_months (36–84), credit_tier.
- research_model: Get the full research payload for a vehicle model (not a specific listing). Returns manufacturer reference data joined with live market data: • All trims with MSRPs, engine/body specs, and EPA fuel economy • NHTSA 5-Star safety ratings and open recall count • Live inventory count
- compare_models: Compare 2-4 vehicle models side by side (model-level, not specific listings). Provide composite make-model slugs like "honda-civic" or "toyota-corolla". Returns: • Winner-by-dimension deltas: price, fuel economy, horsepower, seating, towing, NHTSA safety, live median listing pric
- get_vehicle_rankings: Get data-driven vehicle rankings (e.g., best SUVs, most fuel-efficient cars). Call without arguments to list all ranking categories. Pass a category slug (e.g., "best-suvs") for the full scored ranking. Rankings are computed from public data with a published methodology: NHTSA sa
- get_similar_vehicles: Find similar on-lot vehicles for a VIN ("you may also like"). Same make/model keyword comps as the shop VDP rail (not semantic embeddings). CRITICAL: CITE: Each vehicle's cite object is `vin` + `url` (canonical opendealer.shop VDP). Cite only `url` to shoppers. Never invent VDP U
- ui_select_vehicle: App-only: record a vehicle selection from the results widget. Not for model use — hosts filter via _meta.ui.visibility.
- ui_page_vehicle_results: App-only: paginate or refresh vehicle results using the same Runtime paths as filter_vehicles / search_vehicles (geo-correct). No widget remount — omit resourceUri. Not for model use.

Screening checks:
- MCP handshake: pass (Answered in 734ms)
- Domain against threat feeds (Cloudflare security DNS): pass (mcp.opendealer.app, opendealer.pro not flagged)
- Published packages against the OSV malicious-package database: n/a (No npm or PyPI package published)
- Hidden instructions or invisible characters in tool text: pass (27 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (No redirects off the domain)
- AI review of purpose and tool behavior: pass (No concerns)