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Doctolib

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$9.00/month + usage

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Doctolib

Doctolib

anchor/doctolib

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Anchor

Maintained by Community

Scraping Doctolib is now super easy and cheap! Extract phones, names, contact, timings, image and addresses of medics, doctors, hospitals... Best part : you can even customize what info to extract from Doctolib!

1.0 (1)

Pricing

$9.00/month + usage

5

Monthly users

13

Runs succeeded

87%

Last modified

4 months ago

You can access the Doctolib programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.

1# Start Server-Sent Events (SSE) session and keep it running
2curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=anchor/doctolib"
3
4# Session id example output:
5# event: endpoint
6# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1fa

Using Doctolib scraper via Model Context Protocol (MCP) server

MCP server lets you use Doctolib scraper within your AI workflows. Send API requests to trigger actions and receive real-time results. Take the received sessionId and use it to communicate with the MCP server. The message starts the Doctolib scraper Actor with the provided input.

1curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
2  "jsonrpc": "2.0",
3  "id": 1,
4  "method": "tools/call",
5  "params": {
6    "arguments": {
7      "startUrls": [
8            {
9                  "url": "https://www.doctolib.fr/infectiologue/75001-paris"
10            }
11      ],
12      "pageFunction": "async function pageFunction(context) {\\n\\n    let data = {}\\n    let userData = context.request.userData\\n    data.url = context.request.url\\n    data.label = userData.label\\n    \\n    if(userData && userData.label === '\''doctor'\''){   \\n        data.nom = await context.page.locator('\''#main-content h1'\'').innerText({timeout:6000})\\n        data.tarif = await context.innerTextwrapper(context,'\''#payment_means'\'')\\n        data.horaire_contact = await context.innerTextwrapper(context,'\''#openings_and_contact'\'')\\n        data.description = await context.innerTextwrapper(context,'\''.dl-profile-bio'\'')\\n        data.specialite = await context.innerTextwrapper(context,'\''.dl-profile-header-speciality'\'')\\n        data.expertise = await context.innerTextwrapper(context,'\''#skills'\'')\\n        try{\\n            data.phones = await context.getPhones(data.horaire_contact)\\n        }catch(e){\\n            context.log.info('\''Phones not found'\'',e);     \\n        }\\n        try{\\n            data.image = await context.page.locator('\''.dl-profile img'\'').first().getAttribute('\''src'\'',{timeout:2000})\\n            if(data.image.startsWith('\''/'\'')){ data.image = '\''https:'\'' + data.image}\\n        }catch(e){\\n            context.log.info('\''Image not found'\'',e);     \\n        }        \\n        \\n    }else{\\n        context.log.info('\''we are not on a doctor page: so a search or pagination page.'\'');\\n        userData.label = '\''doctor'\'';\\n        const elements = context.page.locator('\''.search-result-card a[href]'\'');\\n        const links = await elements.evaluateAll(elems => elems.map(elem => elem.getAttribute('\''href'\'')));\\n        let extenstion = '\''fr'\''\\n        if(context.request.url.includes('\''doctolib.de'\'')){ extenstion = '\''de'\'' }\\n        if(context.request.url.includes('\''doctolib.it'\'')){ extenstion = '\''it'\'' }\\n        links.forEach(async link => {\\n            if(link.startsWith('\''/'\'')){ link = `https://www.doctolib.${extenstion}${link}` }\\n            await context.enqueueRequest(link, userData , true);\\n        })\\n\\n    }\\n    context.log.info(`ending this page now`);\\n    delete data.label\\n    return data;\\n}\\n"
13},
14    "name": "anchor/doctolib"
15  }
16}'

The response should be: Accepted. You should received response via SSE (JSON) as:

1event: message
2data: {
3  "result": {
4    "content": [
5      {
6        "type": "text",
7        "text": "ACTOR_RESPONSE"
8      }
9    ]
10  }
11}

Configure local MCP Server via standard input/output for Doctolib scraper

You can connect to the MCP Server using clients like ClaudeDesktop and LibreChat or build your own. The server can run both locally and remotely, giving you full flexibility. Set up the server in the client configuration as follows:

1{
2  "mcpServers": {
3    "actors-mcp-server": {
4      "command": "npx",
5      "args": [
6        "-y",
7        "@apify/actors-mcp-server",
8        "--actors",
9        "anchor/doctolib"
10      ],
11      "env": {
12        "APIFY_TOKEN": "<YOUR_API_TOKEN>"
13      }
14    }
15  }
16}

You can further access the MCP client through the Tester MCP Client, a chat user interface to interact with the server.

To get started, check out the documentation and example clients. If you are interested in learning more about our MCP server, check out our blog post.

Pricing

Pricing model

Rental 

To use this Actor, you have to pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period. You also pay for the Apify platform usage.

Free trial

3 days

Price

$9.00