ESC
Recipes

API Recipes

Pre-built workflows that chain multiple Toolkit API endpoints together. Copy the code, swap in your API key, and run.

Email → DNS → Geo

Lead Enrichment Pipeline

Validate an email address, check the domain's mail infrastructure, then geolocate the mail server to build a rich lead profile.

1

Validate the email address

Email Toolkit — POST /v1/validate

Returns syntax validity, deliverability score, disposable/role-based flags.

2

Look up MX records for the domain

DNS Toolkit — GET /v1/mx?domain=example.com

Returns the mail exchange servers and their priorities.

3

Geolocate the primary mail server

Geo Toolkit — GET /v1/ip-lookup?ip={mx_ip}

Returns country, city, ISP, and coordinates for the mail server IP.

Show Python code 
import httpx

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}

email = "[email protected]"
domain = email.split("@")[1]

# Step 1: Validate email
r1 = httpx.post("https://email.toolkitapi.io/v1/validate",
                headers=HEADERS, json={"email": email})
validation = r1.json()

# Step 2: Get MX records for the domain
r2 = httpx.get(f"https://dns.toolkitapi.io/v1/mx?domain={domain}",
               headers=HEADERS)
mx_records = r2.json()

# Step 3: Geolocate the primary mail server
primary_mx = mx_records["data"]["records"][0]["exchange"]
r3 = httpx.get(f"https://geo.toolkitapi.io/v1/ip-lookup?host={primary_mx}",
               headers=HEADERS)
location = r3.json()

print(f"Email valid: {validation['data']['is_valid']}")
print(f"Mail server: {primary_mx}")
print(f"Server location: {location['data']['city']}, {location['data']['country']}")
DNS + DNS + DNS

Domain Intelligence Report

Pull DNS records, WHOIS registration data, and SSL certificate details for a domain in parallel to build a comprehensive intelligence report.

1

Fetch core DNS records

DNS Toolkit — GET /v1/a, /v1/mx, /v1/ns, /v1/txt

2

Retrieve WHOIS registration data

DNS Toolkit — GET /v1/whois?domain=example.com

3

Check SSL certificate details

DNS Toolkit — GET /v1/ssl?domain=example.com

Show Python code 
import httpx
import asyncio

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}
BASE = "https://dns.toolkitapi.io/v1"
domain = "example.com"

async def domain_report():
    async with httpx.AsyncClient(headers=HEADERS) as client:
        # Run all lookups in parallel
        a, mx, ns, txt, whois, ssl = await asyncio.gather(
            client.get(f"{BASE}/a?domain={domain}"),
            client.get(f"{BASE}/mx?domain={domain}"),
            client.get(f"{BASE}/ns?domain={domain}"),
            client.get(f"{BASE}/txt?domain={domain}"),
            client.get(f"{BASE}/whois?domain={domain}"),
            client.get(f"{BASE}/ssl?domain={domain}"),
        )
    return {
        "dns": {"a": a.json(), "mx": mx.json(), "ns": ns.json(), "txt": txt.json()},
        "whois": whois.json(),
        "ssl": ssl.json(),
    }

report = asyncio.run(domain_report())
print(f"IPs: {report['dns']['a']['data']['records']}")
print(f"Registrar: {report['whois']['data']['registrar']}")
print(f"SSL issuer: {report['ssl']['data']['issuer']}")
SEO → Scrape → Image

Website Audit Workflow

Run an SEO audit, scrape the page content for analysis, and capture a visual screenshot — all from a single URL.

1

Run full SEO audit

SEO Toolkit — POST /v1/audit

Returns meta tags, headings, Open Graph, structured data, and page speed metrics.

2

Scrape page content as Markdown

Scrape Toolkit — POST /v1/scrape

Returns clean Markdown text suitable for LLM ingestion or content analysis.

3

Generate a visual screenshot

Image Toolkit — POST /v1/screenshot

Returns a full-page PNG screenshot of the URL.

Show Python code 
import httpx

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}
url = "https://example.com"

# Step 1: SEO audit
r1 = httpx.post("https://seo.toolkitapi.io/v1/audit",
                headers=HEADERS, json={"url": url}, timeout=30)
audit = r1.json()

# Step 2: Scrape page content
r2 = httpx.post("https://scrape.toolkitapi.io/v1/scrape",
                headers=HEADERS, json={"url": url}, timeout=30)
content = r2.json()

# Step 3: Screenshot
r3 = httpx.post("https://image.toolkitapi.io/v1/screenshot",
                headers=HEADERS, json={"url": url}, timeout=30)
# Save the screenshot
with open("screenshot.png", "wb") as f:
    f.write(r3.content)

print(f"Title: {audit['data']['meta']['title']}")
print(f"SEO score: {audit['data']['score']}")
print(f"Content length: {len(content['data']['markdown'])} chars")
print("Screenshot saved to screenshot.png")
Convert → PDF → Image

Document Processing Pipeline

Convert Markdown documentation to HTML, generate a PDF, and create a thumbnail preview image — a common publishing workflow.

1

Convert Markdown to HTML

Convert Toolkit — POST /v1/markdown-to-html

2

Generate PDF from the HTML

PDF Toolkit — POST /v1/from-html

3

Create a thumbnail of the first page

Image Toolkit — POST /v1/resize

Show Python code 
import httpx
import base64

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}

markdown = """# Project Report
## Summary
This quarter we shipped 3 major features...
"""

# Step 1: Markdown → HTML
r1 = httpx.post("https://convert.toolkitapi.io/v1/markdown-to-html",
                headers=HEADERS, json={"markdown": markdown})
html = r1.json()["data"]["html"]

# Step 2: HTML → PDF
r2 = httpx.post("https://pdf.toolkitapi.io/v1/from-html",
                headers=HEADERS, json={"html": html}, timeout=30)
with open("report.pdf", "wb") as f:
    f.write(r2.content)

# Step 3: Create thumbnail (resize first page image)
r3 = httpx.post("https://image.toolkitapi.io/v1/resize",
                headers=HEADERS,
                json={"url": "report-page-1.png", "width": 300})
with open("thumbnail.png", "wb") as f:
    f.write(r3.content)

print("Pipeline complete: report.pdf + thumbnail.png")
Email → Auth → Auth

Secure User Onboarding

Validate a new user's email, hash their password securely, and generate a TOTP secret for two-factor authentication — all in one flow.

1

Validate the user's email

Email Toolkit — POST /v1/validate

Reject disposable or invalid addresses before creating the account.

2

Hash the password with bcrypt

Auth Toolkit — POST /v1/hash/bcrypt

Returns a salted bcrypt hash ready to store in your database.

3

Generate a TOTP secret for 2FA

Auth Toolkit — POST /v1/totp/generate

Returns a secret key and QR code URI for authenticator apps.

Show Python code 
import httpx

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}

email = "[email protected]"
password = "s3cure-p@ssw0rd"

# Step 1: Validate email first
r1 = httpx.post("https://email.toolkitapi.io/v1/validate",
                headers=HEADERS, json={"email": email})
if not r1.json()["data"]["is_valid"]:
    raise ValueError("Invalid email address")

# Step 2: Hash password
r2 = httpx.post("https://auth.toolkitapi.io/v1/hash/bcrypt",
                headers=HEADERS, json={"text": password})
password_hash = r2.json()["data"]["hash"]

# Step 3: Generate TOTP secret for 2FA
r3 = httpx.post("https://auth.toolkitapi.io/v1/totp/generate",
                headers=HEADERS, json={"issuer": "MyApp", "account": email})
totp = r3.json()["data"]

# Store in your database:
user = {
    "email": email,
    "password_hash": password_hash,
    "totp_secret": totp["secret"],
    "totp_qr_uri": totp["uri"],
}
print(f"User created: {email}")
print(f"2FA QR URI: {totp['uri']}")
Scrape → DevTools → Convert

Content Pipeline for LLMs

Scrape a web page into structured data, extract and format the metadata as JSON, then convert to YAML for your LLM training config.

1

Scrape page and extract metadata

Scrape Toolkit — POST /v1/metadata

Returns title, description, Open Graph tags, and structured data.

2

Pretty-print the JSON

Dev Toolkit — POST /v1/format/json

Formats and validates the JSON structure for inspection.

3

Convert to YAML for config files

Convert Toolkit — POST /v1/json-to-yaml

Outputs clean YAML ready for LLM training configs or documentation.

Show Python code 
import httpx
import json

API_KEY = "your-api-key"
HEADERS = {"X-API-Key": API_KEY}

url = "https://example.com/blog/post-1"

# Step 1: Extract structured metadata
r1 = httpx.post("https://scrape.toolkitapi.io/v1/metadata",
                headers=HEADERS, json={"url": url}, timeout=30)
metadata = r1.json()["data"]

# Step 2: Format the JSON (validate + pretty-print)
r2 = httpx.post("https://dev.toolkitapi.io/v1/format/json",
                headers=HEADERS, json={"input": json.dumps(metadata)})
formatted = r2.json()["data"]["formatted"]

# Step 3: Convert JSON to YAML
r3 = httpx.post("https://convert.toolkitapi.io/v1/json-to-yaml",
                headers=HEADERS, json={"json": json.dumps(metadata)})
yaml_output = r3.json()["data"]["yaml"]

print("--- YAML output ---")
print(yaml_output)

Build your own workflows

Every endpoint is a standard REST call. Mix and match toolkits to build exactly the pipeline your project needs.

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