# Fnac MCP Server (`axlymxp/fnac-mcp-server`) Actor MCP server + scraper for fnac.com. Search products, get full detail with marketplace offers, autocomplete, and recommendations live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Fnac scraper. - **URL**: https://apify.com/axlymxp/fnac-mcp-server.md - **Developed by:** [axly](https://apify.com/axlymxp) (community) - **Categories:** Developer tools, E-commerce - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Fnac MCP Server Give your AI assistant **live access to Fnac.com** (French electronics, books & entertainment retailer). This actor runs as a **Model Context Protocol (MCP) server** so **Claude, ChatGPT, Cursor, n8n** and other agents can search products, pull full detail with marketplace offers, get autocomplete suggestions, and fetch recommendations — in real time. It also runs as a **classic scraper** when you just want a dataset. Powered by a self-contained hybrid client: no external paid unblocking subscription required. ### Who it's for - **AI-assistant / agent builders** — add live Fnac product and price lookup to a shopping or price-comparison agent. - **Analysts using Claude/Cursor** — ad-hoc price/availability/marketplace checks in chat without building a scraper. - **Automation (n8n/Make) builders** — MCP tool nodes for price-monitoring or repricing workflows. ### MCP tools | Tool | What it does | | ------------------------------ | -------------------------------------------------------------------------- | | `search_fnac_products` | Keyword search → normalized rows, optionally enriched with full detail | | `get_fnac_product` | Full detail for one product, including per-seller marketplace offers | | `fnac_autocomplete` | Search-box autocomplete suggestions | | `get_fnac_recommendations` | "Customers also bought" / related products for a given product | ### Output fields (per product) `prid`, `title`, `brand`, `price`, `base_price`, `currency`, `gtin`, `sku`, `mpn`, `condition`, `seller`, `seller_type`, `availability`, `category`, `rating`, `reviews_count`, `num_offers`, `offers` (full per-seller breakdown), `characteristics`, `images`, `url`. ### Connect it 1. Start the actor in **Standby mode** on Apify. 2. Point your MCP client at `https:///mcp` (Streamable HTTP). 3. The tools above appear automatically in your AI assistant. Example (Claude Desktop / Cursor MCP config): ```json { "mcpServers": { "fnac": { "url": "https:///mcp" } } } ```` ### Classic scraper mode Prefer a plain dataset? Run the actor normally with an input like: ```json { "searchQueries": ["iphone"], "maxPagesPerQuery": 1, "fetchOffers": true } ``` It pushes normalized product rows to the dataset — identical fields to the tools. ### High-value use cases - **Conversational price checks** — "what's the cheapest marketplace offer for this PlayStation 5?" answered live by your assistant. - **Competitive intelligence agents** — an agent pulls every seller's offer for a product before recommending where to buy. - **Procurement/monitoring automation** — an n8n/Make workflow polls prices and marketplace offers for tracked products. ### FAQ **Do I need proxies or an API key?** No — the actor solves Fnac's anti-bot challenge itself with a built-in stealth browser. **How fresh is the data?** Live on every tool call. **Is `search_fnac_products` 100% reliable?** Fnac's search page sits behind its strictest anti-bot tier and can occasionally return zero results on a given call even with automatic retries. `get_fnac_product` (by product ID or URL) uses a separate, consistently reliable channel and is not affected. **Can it return a dataset too?** Yes — run it in normal mode for a classic scrape, using the same fields as the tools. **Is scraping legal?** The actor collects only publicly available product data. You are responsible for complying with Fnac's terms and applicable laws. # Actor input Schema ## `searchQueries` (type: `array`): Keywords to search (classic run mode only — ignored when connected as an MCP server). ## `productUrls` (type: `array`): Direct Fnac product URLs to fetch (classic run mode only). ## `maxPagesPerQuery` (type: `integer`): Search pages per keyword in classic run mode. ## `fetchOffers` (type: `boolean`): Include the full per-seller marketplace offers array in each row. ## Actor input object example ```json { "searchQueries": [ "iphone" ], "productUrls": [], "maxPagesPerQuery": 1, "fetchOffers": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("axlymxp/fnac-mcp-server").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("axlymxp/fnac-mcp-server").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call axlymxp/fnac-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=axlymxp/fnac-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fnac MCP Server", "description": "MCP server + scraper for fnac.com. Search products, get full detail with marketplace offers, autocomplete, and recommendations live from Claude, ChatGPT, Cursor and other AI agents — or run it as a classic Fnac scraper.", "version": "1.0", "x-build-id": "NUGyWYDrsjRs0xh8S" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/axlymxp~fnac-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-axlymxp-fnac-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/axlymxp~fnac-mcp-server/runs": { "post": { "operationId": "runs-sync-axlymxp-fnac-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/axlymxp~fnac-mcp-server/run-sync": { "post": { "operationId": "run-sync-axlymxp-fnac-mcp-server", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "searchQueries": { "title": "Search queries", "type": "array", "description": "Keywords to search (classic run mode only — ignored when connected as an MCP server).", "default": [], "items": { "type": "string" } }, "productUrls": { "title": "Product URLs", "type": "array", "description": "Direct Fnac product URLs to fetch (classic run mode only).", "default": [], "items": { "type": "string" } }, "maxPagesPerQuery": { "title": "Max search pages per query", "minimum": 1, "maximum": 5, "type": "integer", "description": "Search pages per keyword in classic run mode.", "default": 1 }, "fetchOffers": { "title": "Include marketplace offers", "type": "boolean", "description": "Include the full per-seller marketplace offers array in each row.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```