Poetry URL Classification API Integration Checklist

You need to detect poetry-related websites at scale so you can block or prioritize them, enrich user signups with literary context, or audit where your ads show up next to poetry content. By the end of this guide, you’ll have a working request to Klazify’s categorization endpoint, a small parsing script, and a production checklist to classify poetry URLs reliably in your data pipeline.

Why Klazify excels at classifying poetry content on the web

Poetry sites span individual poet blogs, literary magazines, teaching resources, and community forums. These are often text-heavy, multilingual, and updated frequently. Klazify’s feature set maps cleanly to this challenge:

• Accurate website categorization using AI: The API analyzes full website content, not just metadata. This is critical for poetry pages where the signal is in long-form text and headings rather than structured tags.
• Global coverage: Poetry content appears in many languages; the API can analyze multilingual pages so you can handle international literary magazines and non-English poetry communities.
• Real-time classification: Poetry sites and pages change often (new issues, new poems). Klazify performs fresh analysis instead of relying solely on stale lookups.
• Industry-level categories: Results map to IAB taxonomy. You can align poetry and literature within the broader arts-related hierarchy that your ad tech or content systems already recognize.
• Simple API integration: A single REST call classifies a URL or domain and can also return company data, logo URLs, related domains, and more—useful when building a literary publisher profile.
• Compliance and filtering: If you run brand safety or content filtering, you can whitelist poetry content or set adjacency rules so ads appear in appropriate contexts.

In practice, teams plug these results into ad tech, content filtering, CRM enrichment, cybersecurity, and analytics workflows. For a poetry-focused use case, you can: allowlist literary magazines for ad adjacency, route poetry submissions to the right editorial queue, or enrich user-entered domains on signup to detect whether they represent a poet’s site or a publisher.

Explore the platform at klazify.com and follow along to get a working classification in minutes.

Make your first classification request

Klazify’s main endpoint classifies a URL or domain and returns categories, optional company information, logo URL, similar domains, and domain registration data—all in one response. You’ll use a Bearer token and send a JSON body with a single field: “url”.

cURL request (label: Request)

curl -X POST "https://www.klazify.com/api/categorize" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url":"https://cbsnews.com"}'

Replace YOUR_API_KEY with your token. In your system, you’ll pass the real poetry URL (e.g., your magazine issue page) instead of cbsnews.com. This format is identical; the endpoint classifies at the given URL.

Official JSON example (label: Response)

{
"domain": {
"categories": [
{
"confidence": 0.92,
"name": "/Computers & Electronics/Consumer Electronics",
"IAB-632-596": "Consumer Electronics/Technology & Computing/Consumer Electronics"
},
{
"confidence": 0.89,
"name": "/Internet & Telecom/Mobile & Wireless/Mobile Phones"
}
],
"social_media": null,
"logo_url": "https://klazify.s3.amazonaws.com/2110787991611585019600ed5fb1d1300.04730104.png"
},
"success": true,
"objects": {
"company": {
"url": "https://www.apple.com/",
"name": "Apple",
"city": "Cupertino",
"stateCode": "CA",
"countryCode": "US",
"employeesRange": "100K+",
"revenue": 274515000000,
"raised": null,
"tags": [
"E-commerce",
"Consumer Electronics",
"Mobile",
"B2C"
],
"tech": [
"omniture_adobe_analytics",
"atlassian_confluence",
"successfactors",
"apache_apex",
"talend",
"oracle_peoplesoft",
"salesforce",
"stripe",
"dell_boomi_atomsphere",
"gigya",
"sage_50cloud",
"quickbooks",
"webmethods",
"apache_tomcat",
"alteryx",
"tibco_rendezvous",
"atlassian_jira",
"..."
]
}
},
"domain_registration_data": {
"domain_age_date": "1987-02-19",
"domain_age_days_ago": "13026",
"domain_expiration_date": "2030-02-20",
"domain_expiration_days_left": "123"
},
"similar_domains": [
"bestbuy.com",
"icloud.com",
"microsoft.com",
"macrumors.com",
"google.com",
"samsung.com",
"twitter.com",
"hp.com",
"bhphotovideo.com",
"dell.com"
]
}

How you’ll use it for poetry classification:

• domain.categories: Use name and confidence to decide whether a page aligns with your poetry-related logic. The name value follows a hierarchical structure compatible with IAB mapping keys when present.
• objects.company: If the URL belongs to a publisher or press, details like name, tags, and tech can help you enrich CRM records or confirm it’s a known literary organization.
• domain.logo_url: Useful for UI previews when reviewers validate poetry sites or when you display publisher branding in dashboards.
• domain_registration_data: Age and expiration can inform trust heuristics when handling unknown literary websites.
• similar_domains: Discover related properties that may also publish poetry, useful for expanding allowlists or doing outreach.

Browse the full API surface in the Documentation. If you need a free trial to follow along, you can Register now.

Minimal Python example to classify and route

import json
import requests

API_KEY = "YOUR_API_KEY"
API_URL = "https://www.klazify.com/api/categorize"

def classify_url(url: str) -> dict:
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
}
payload = {"url": url}
r = requests.post(API_URL, headers=headers, data=json.dumps(payload), timeout=20)
r.raise_for_status()
return r.json()

def extract_key_fields(resp: dict) -> dict:
# Pull the highest-confidence category, if any
categories = resp.get("domain", {}).get("categories", []) or []
top_category = max(categories, key=lambda c: c.get("confidence", 0), default=None)
company = resp.get("objects", {}).get("company", {}) or {}
return {
"success": resp.get("success", False),
"top_category_name": (top_category or {}).get("name"),
"top_category_confidence": (top_category or {}).get("confidence"),
"iab_mapping": (top_category or {}).get("IAB-632-596"),
"logo_url": resp.get("domain", {}).get("logo_url"),
"company_name": company.get("name"),
"company_tags": company.get("tags", []),
"similar_domains": resp.get("similar_domains", []),
"domain_age_date": resp.get("domain_registration_data", {}).get("domain_age_date"),
}

def route_for_poetry(fields: dict) -> str:
# Example: treat poetry-like signals via an internal taxonomy mapping.
# Avoid hard-coding category names; use your own normalized rules.
name = fields["top_category_name"] or ""
confidence = fields["top_category_confidence"] or 0.0

# Pseudologic: if category path indicates arts/literature-like content at sufficient confidence
# In your production code, implement a deterministic mapping table (see section below).
poetry_like = ("Arts & Entertainment" in name) and (confidence >= 0.75)

if poetry_like:
return "allowlist:poetry"
if confidence == 0.0 or not name:
return "review:unknown"
return "neutral"

if __name__ == "__main__":
resp = classify_url("https://cbsnews.com")
fields = extract_key_fields(resp)
decision = route_for_poetry(fields)
print(json.dumps({"fields": fields, "decision": decision}, indent=2))

This snippet reads domain.categories, selects the highest-confidence category, pulls optional IAB mapping when present, and shows how you might route poetry-like content using internal rules. Replace the condition with your own taxonomy mapping.

Turn classification into actions for poetry-focused workflows

Most teams map URL classification to one or more operational decisions. Below are common outcomes and which fields to consult:

Production checklist: accuracy, throughput, and resilience

• Batching: Group URLs by eTLD+1 to improve cache hits and network efficiency. Serialize requests where needed to avoid hammering the same host during fetches.
• Caching: Cache by normalized domain or full URL depending on your need for page-level granularity. For fast-changing pages (e.g., issue landing pages), use a shorter TTL; for static poet bios or archive pages, use a longer TTL. Reuse cached responses across your ETL and API layers.
• Backoff and retries: Implement exponential backoff for transient network failures and HTTP 5xx. Avoid retry storms by bounding attempts and honoring server errors.
• Confidence thresholds: Require a minimum confidence for automated allowlisting. Send low-confidence results to a review queue with the logo_url and category path for quick adjudication.
• Normalization: Lowercase and trim categories when matching, but keep the original value for audit logs. Persist both raw and normalized fields.
• Idempotency: Deduplicate work by storing a content hash (domain + path) and last classification timestamp so reprocessing is predictable.
• Observability: Log success, latency, and non-2xx responses. Attach request IDs in your logs to trace issues quickly.

Handling unknown, new, or mixed-content poetry domains

Not every poetry site fits a single category, and some domains are new or sparsely populated. Build guardrails:

• Empty or null categories: If domain.categories is empty, send to manual review. Store the attempt and retry on a timed schedule, respecting your cache TTL.
• Low confidence: If the top category confidence is below your threshold, classify as “review:unknown” and enrich with domain_registration_data for prioritization.
• Mixed content domains: Classify at the URL level for precise results on specific poetry pages instead of the broader domain root.
• Scaling to new finds: Feed similar_domains into your crawl queue, but filter via your mapping to keep things poetry-focused.

Mapping Klazify categories to a “Poetry” taxonomy

Most pipelines define a custom taxonomy with a “Poetry” tag and potentially sub-tags (e.g., “Publishers,” “Educational,” “Community”). Use a rule-based mapper:

• Positive signals: category path segments that clearly indicate arts-related themes, plus high-confidence scores; known literary publisher domains in your allowlist; business tags associated with publishing from objects.company.tags when available.
• Negative signals: categories unrelated to arts or literature; domains where similar_domains are mostly off-topic.
• Ties and uncertainty: route to review if confidence is below threshold or signals conflict. Show logo_url and the category path to speed up decisions.

This process avoids hard-coding brittle category names. Instead, normalize category paths, whitelist trusted literary organizations, and keep a separate mapping file that your data team can update without code changes.

Operational notes: usage, billing, and MCP

• Plan and trial: Starter is $39.99/month with a 7-day trial. Failed or unreachable calls are not billed.
• Rate limits: Implement client-side batching and backoff. If your workload spikes (e.g., reclassifying a poetry archive), queue and pace requests.
• MCP endpoint: The MCP base is available at MCP. Note that performing a GET on /mcp (https://mcp.klazify.com, GET /mcp) returns 405.
• Security: Store API keys in a secrets manager and rotate regularly. Prefer HTTPS everywhere and short timeouts with retries.

Visit klazify.com to explore the platform surface area and supported fields beyond categorization, including company enrichment and technology signals.

Putting it together in your pipeline

A typical poetry URL workflow in a data platform looks like this:

1. Ingest: Collect candidate URLs from submissions, sitemaps, or referrers.
2. Normalize: Canonicalize scheme, host, and path; group by eTLD+1 for batching.
3. Cache check: If a fresh cached categorization exists, use it. Otherwise, call the API.
4. Classify: POST to /api/categorize; log success and latency; handle retries if needed.
5. Map: Apply your poetry taxonomy mapping using domain.categories, confidence, and optional IAB mapping key when present.
6. Enrich: For poetry publishers, merge objects.company, logo_url, and similar_domains into CRM or indexes.
7. Decide: Allowlist, block, or queue for manual review depending on thresholds and business rules.
8. Store: Persist raw JSON and normalized fields; set TTL for rechecks.
9. Monitor: Track classification coverage and review queue size to tune thresholds.

Troubleshooting tips that save time

• Intermittent timeouts: Increase client timeout modestly (e.g., to 20–30s) and retry with exponential backoff. Record failure reason; retries should not be unbounded.
• Unexpected category drift: Cache busting may be needed for pages that changed content. Confirm you’re classifying the exact URL (not an outdated redirect).
• Too many manual reviews: Raise confidence thresholds only after sampling real poetry pages. Often, adding a few known publisher allowlist rules reduces review volume dramatically.
• Pipeline duplication: Use a content hash key (URL + last-modified if known) to prevent redundant reclassification during bulk reprocessing.

FAQ

Does the API classify at page level or domain level?
You send a URL and receive categorization for that address. Use page-level classification for mixed-content sites and domain-level when you want broader treatment.

How do I handle newly created poetry domains with little content?
Use domain_registration_data to flag very new registrations and route to manual review when confidence is low or categories are missing. Recheck after your cache TTL expires.

Can I align results with my own “Poetry” taxonomy?
Yes. Create a mapping layer from domain.categories (and IAB mapping when present) to your internal tags. Keep rules in a data table so the business team can adjust without code changes.

What happens if a request fails?
Implement retries with backoff for transient failures. Failed or unreachable calls are not billed. Log the error, do not block the ingestion pipeline; instead, schedule a reattempt.

Where do I find all fields and capabilities?
See the Documentation for endpoint details, fields, and response structures.

Ready to classify poetry URLs and ship your integration? Create your free trial account and get an API key here: Register. You can also explore more at klazify.com and review the MCP page if you need that surface.