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Use Cases

Build a Lead Generation Agent

A lead generation agent takes a request like "Marketing directors at French SaaS companies with 50 to 200 employees" and returns a list of matching people with verified professional emails. Evaboot provides every step: building the Sales Navigator search, extracting the leads, and finding their emails.

How it works

1. Describe the audience. The search builder maps the description onto real Sales Navigator filters (title, seniority, industry, headcount, region, revenue) and returns a search URL.
2. Check the budget. Your quota and credit balance tell you how many leads you can export today.
3. Export the search. Evaboot extracts every lead behind the URL and finds a verified email for each one where it exists.
4. Use the results. Read them from the API, send them to a connected CRM, or hand them back to the user.

With an AI assistant (MCP)

Connect your assistant to https://mcp.evaboot.com/mcp. The MCP page of the Evaboot app gives the steps for each assistant (also on the MCP Server page):

Then ask in plain language:

"Find 100 marketing directors at French SaaS companies with 50 to 200 employees and export them with emails."

The assistant chains the tools itself:

Step	Tool
Build the search	build_sn_search_from_prompt
Check quota and credits	get_daily_export_quota
Export the leads	export_sn_search_or_list with max_results
Wait for results	get_export
Send them to a CRM	push_export

Nothing else to deploy. This works in Claude, ChatGPT, Grok, Cursor and any MCP client.

With the API

The same flow from your own code. Every request takes Authorization: Bearer YOUR_API_KEY.

Code
import time
import requests

API = "https://api.evaboot.com/v1"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}

# 1. Description to Sales Navigator search URL (free, synchronous)
search = requests.post(
    f"{API}/search-builder/",
    headers=HEADERS,
    json={
        "description": "Marketing directors at French SaaS companies, 50-200 employees",
        "search_type": "LEAD",
    },
).json()
print(search["filters_used"])

# 2. Launch the extraction (1 credit per lead, email included)
job = requests.post(
    f"{API}/extractions/url/",
    headers=HEADERS,
    json={"linkedin_url": search["url"], "search_name": "FR SaaS marketing directors"},
).json()

# 3. Poll until done: 202 while running, 200 when finished
while True:
    r = requests.get(f"{API}/extractions/{job['extraction_id']}/", headers=HEADERS)
    if r.status_code == 200:
        break
    time.sleep(30)

leads = r.json()["prospects"]

Instead of polling, pass a webhook_url to POST /v1/extractions/url/ and Evaboot POSTs the same results to it when the export finishes.

Tips

• Be specific in the description. Title, seniority, industry, headcount, location and revenue all map to filters. filters_used shows what was understood, so you can check it before spending credits.
• One export returns at most 2,500 leads (1,000 companies), a Sales Navigator limit. Through MCP, the agent can split a larger audience into several searches after asking you.
• Already have a list? Skip the search builder and pass a lead or account list URL straight to the extraction.

Next: keep the list fresh automatically with an Automated Lead Engine.