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serpapi-core-workflow-a

serpapi-core-workflow-a

Build a reproducible Google Search workflow that validates parameters, optional result sections, and bounded pagination. Use when implementing search, SEO monitoring, or evidence collection. Trigger with "build a SerpAPI Google workflow".

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Skill-Dokumentation

GitHub

name: serpapi-core-workflow-a description: 'Build a reproducible Google Search workflow that validates parameters, optional result sections, and bounded pagination. Use when implementing search, SEO monitoring, or evidence collection. Trigger with "build a SerpAPI Google workflow".' argument-hint: "[query] [location] [page-budget]" allowed-tools: Read, Glob, Grep, WebFetch, Write, Edit version: 1.6.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT tags: [saas, serpapi, google-search, pagination, seo] model: inherit effort: high compatibility: Designed for Claude Code; live searches and multi-page retrieval consume account allowance unless a matching server cache is served

SerpAPI Google Search Workflow

Overview

Turn a search question into a bounded, reproducible Google Search request and normalize only the result sections the application actually needs.

Prerequisites

  • A business question, permitted query class, geography, language, device, and freshness target
  • An authenticated official client behind a server-side gateway
  • A search/page budget and fixtures for expected optional sections

Tool Discipline

Use Read, Glob, and Grep to inspect call sites and result consumers, WebFetch to verify current Google parameters and schemas, and Write or Edit for request builders, normalizers, tests, and redacted receipts.

Current Contract

Google Search uses engine=google and q. Locale and device inputs include location, hl, gl, and device; pagination commonly uses start. JSON sections such as organic_results, answer_box, knowledge_graph, related_questions, and local results are query-dependent and optional.

Authentication

The server-side gateway supplies SERPAPI_KEY. Exclude the key and key-bearing URLs from application output, cache keys, logs, telemetry, fixtures, and error reports.

Instructions

  1. Convert the business question into a minimal query and document permitted use, location, language, device, safe-search setting, freshness, and requested fields.
  2. Validate parameters against the current Google Search API rather than copying options from another engine.
  3. Estimate the maximum searches, check the account budget, and present the live execution boundary.
  4. Execute the first page and require a terminal Success or Error status before parsing sections.
  5. Normalize each required section independently and retain provenance fields such as position, source link, and search ID.
  6. Follow serpapi_pagination.next or a documented start offset only while results continue and the page budget remains.
  7. Reconcile counts, deduplicate stable links, test empty and missing-section fixtures, and store a redacted receipt.

Output

Return normalized parameters, result-section schema, requested records with provenance, pages and searches consumed, termination reason, search IDs, and fixture coverage.

Error Handling

ConditionResponse
Success without organic_resultsInspect documented alternative sections and result-state fields.
Location is not resolved as intendedUse a canonical location from the Locations API and record the resolved value.
Page has no continuationStop successfully; never synthesize the next offset blindly.
Schema changesQuarantine the response, update the narrow adapter and fixture, then replay offline.

Example

params = {
    "engine": "google",
    "q": "site:example.com release notes",
    "location": "Austin, Texas, United States",
    "hl": "en",
    "gl": "us",
    "safe": "active",
}
result = client.search(params)
if result["search_metadata"]["status"] != "Success":
    raise RuntimeError(result.get("error", "search did not complete"))
records = [
    {"position": row.get("position"), "title": row.get("title"), "link": row.get("link")}
    for row in result.get("organic_results", [])
]

Resources

Next Steps

Freeze the normalized schema in fixtures and set a page budget appropriate to the product use case.

Gespeicherte Skills