Automating indexing with an API

Why automate

Manual indexing works for a few dozen links a month. It breaks down when an agency receives placements from several vendors daily, when a publisher ships hundreds of pages a week, or when results need to land in a client dashboard. An API lets the same steps run from a script, a spreadsheet job or a reporting system without anyone pasting URLs into a web form.

This page describes vendor indexer APIs. It is not about Google's Indexing API, which Google's quickstart limits to pages with JobPosting or BroadcastEvent embedded in a VideoObject.

The generic loop

1. Collect. Pull new URLs from wherever they appear: a link tracker, a vendor's delivery sheet, a CMS publish hook or a sitemap diff.
2. Filter. Drop duplicates, URLs already indexed, non-200 responses and pages with noindex. Every URL removed here is a credit saved.
3. Submit. Send the batch to the indexer, choosing a mode per batch (bulk standard, urgent instant, or spread over days).
4. Store the job ID. Most indexer APIs are asynchronous: you create a job and poll for its status.
5. Wait. Indexing decisions take time. Checking an hour after submission mostly measures noise.
6. Check. Run an index check on the same URLs.
7. Act. Resubmit misses once; flag repeat misses for manual review; write results to your report.

The filter and act steps are where most of the value is. A pipeline that submits everything and never checks is just spending.

How the IndexChex API maps to the loop

IndexChex exposes a REST API authenticated with per-user API keys. It has two versions.

v1 covers index-check jobs, index-submit jobs and account balance. A pipeline creates a submission job, reads its status, and creates a check job later. Endpoints and payloads are documented on the IndexChex API v1 reference.

v2 mirrors SpeedyIndex's API schema. Teams with an existing SpeedyIndex integration can point it at IndexChex by changing the base URL, as described on the v2 compatibility page. This is useful for agencies whose tooling was written against that schema and who want to compare results without rewriting the client.

One constraint to design around: drip feed and a scheduled re-check cannot be combined on the same job, so drip jobs need a separate check job created by your pipeline.

Reading check results

The checker queries live Google results with four forms (bare URL, quoted URL, site: and inurl:) and counts a URL as indexed if any returns it. IndexChex's weekly measurements on recurring URL sets put the checker at about 96% accurate on average as of October 2026. A pipeline should therefore treat a single "not indexed" result as a strong signal rather than certainty, and confirm important misses before escalating them to a vendor or client.

Design notes

• Idempotency. Keep a table of URL, job ID, mode and last result so a rerun doesn't resubmit the same URLs.
• Separate owned and third-party URLs. Owned pages should go through sitemaps first; see indexing new blog posts.
• Group by client or campaign. Reporting is far simpler when each job maps to one client, as covered in link indexing for agencies.
• Budget guard. Read the balance before submitting and stop when a run would exceed a limit.
• Rate of resubmission. One retry is usually enough; repeated retries rarely help a page Google declined.

Common inputs to such pipelines include guest posts (indexing guest post backlinks) and programmatic page samples (indexing programmatic pages).