TL;DR
Search Results Indexing is a data workflow that chains Tavily + Supabase to automate a common task. Run Tavily searches on scheduled topics and index the results in Supabase for trend analysis and content research. Once configured, it runs through Claude Code, Cursor, Windsurf or any MCP-compatible AI agent.
Search Results Indexing
Run Tavily searches on scheduled topics and index the results in Supabase for trend analysis and content research.
How it works
Run with MCPizy
NewExecute this recipe in your browser — no local install, no Claude Code. Streams results live.
Why this combo?
Tavily is built for AI-grade search with rich metadata; Supabase gives you a durable, queryable store for those results. Running scheduled searches and indexing them lets you track how a topic evolves over time — something a one-off web search can never give you.
Without this workflow
Google a topic manually, skim results, paste interesting links into a doc, lose track of what you found last week.
With MCPizy
Tavily indexes top results on schedule. Query Supabase to see how coverage of a topic changed over the past 30 days.
Business value
Concrete ROI — not marketing fluff.
Time saved
- PR teams catch brand mentions within hours — reply windows stay open, reputation risk drops
- SEO teams track SERP changes over time — measure the real impact of content and backlink campaigns
Workflow steps
- 1Trigger search for configured keywords
- 2Fetch top Tavily results with metadata
- 3Normalize titles, URLs, and snippets
- 4Store in Supabase with timestamp and topic tag
- 5Deduplicate URLs across runs
Use cases
- Track how media coverage of your brand evolves week over week
- Index competitor mentions and analyze sentiment trends
- Build a research corpus on a topic by accumulating daily search results
- Alert when a new source starts covering your target keywords
Agent prompt (copy into Claude Code)
This prompt is the workflow. Paste into Claude Code, Cursor, or Windsurf.
You are a search-indexing agent. Runs daily for each topic in topics.json. For each topic: 1. Call tavily.search(query=topic.query, search_depth="advanced", max_results=20, include_domains=topic.include, exclude_domains=topic.exclude) 2. For each result, normalize: url, title, snippet, published_date, source_domain 3. Dedupe against Supabase table tavily_results by url hash — skip if already stored 4. Batch-insert new rows via supabase.execute_sql with INSERT INTO tavily_results (topic, url, title, snippet, published_date, indexed_at) VALUES (...) 5. If new_count >= topic.alert_threshold, log a row in alerts table and tag the topic as "active" Report per-topic counts: new | total | velocity_7d.
Trigger & credentials
How this workflow fires and what env vars you need.
0 8 * * * # every day at 08:00 UTC
One-command deploy
Install everything — MCPs, prompt, env template — in a single call.
$ mcpizy recipe install tavily-supabase-search-indexing ✓ Installs all 2 MCP servers ✓ Writes prompt to ~/.mcpizy/prompts/tavily-supabase-search-indexing.md ✓ Generates .env.example in current directory ✓ Ready to paste into Claude Code
Requires mcpizy CLI v1.1+ — install via npm i -g mcpizy.
Quick install (MCPs only)
$ mcpizy install tavily && mcpizy install supabaseMore Data recipes
Web Scraping to Database
Schedule a Firecrawl scrape of any website and store the structured results directly in a Supabase table for analysis.
Cache Invalidation Pipeline
When a Supabase row changes, the corresponding Redis cache key is automatically invalidated to keep your API fresh.
Knowledge Graph from Code
Parse your GitHub repos and build a Neo4j knowledge graph of files, functions, imports, and authors for code intelligence.
Data Lake Queries
Query Parquet files directly from S3 using DuckDB without any ETL. Results are returned in seconds for ad-hoc analytics.
Frequently asked questions
What is this workflow?
Search Results Indexing is a data automation that uses Tavily + Supabase together via the Model Context Protocol. Run Tavily searches on scheduled topics and index the results in Supabase for trend analysis and content research.
How long does setup take?
Setup takes around 8 min setup, continuous trend tracking. You install the required MCP servers with `mcpizy install tavily && mcpizy install supabase`, connect your accounts, and the workflow is ready to run.
What does this workflow change in practice?
PR teams catch brand mentions within hours — reply windows stay open, reputation risk drops; SEO teams track SERP changes over time — measure the real impact of content and backlink campaigns.
Which MCP servers do I need for this?
You need 2 MCP servers: Tavily (mcpizy install tavily), Supabase (mcpizy install supabase). All are installable in one command via the MCPizy CLI and configured in your `.claude.json` or `.cursor/mcp.json`.
Does this work with Claude Code, Cursor, and Windsurf?
Yes. The workflow runs with any MCP-compatible AI agent — Claude Code, Claude Desktop, Cursor, Windsurf, VS Code with Copilot, and custom agents built on the MCP SDK. The MCP servers are identical across clients; only the config file path (`.claude.json` vs `.cursor/mcp.json`) changes.
Start building this workflow
Install the required MCPs from the directory and automate this in 8 min setup.
$ mcpizy install tavily && mcpizy install supabase
Free to install. Connect your accounts and this workflow runs itself.