Build Serverless Data Warehouses Using MCP.
You scrape data into CSV files that nobody queries , Firecrawl extracts structured web data, Neon stores it in serverless PostgreSQL you can query with SQL, and Sheets visualizes the results
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your agent builds a data warehouse from the web. Step 1: Firecrawl scrapes structured data , product listings, pricing, company information, job postings , from target websites.
Handles JavaScript, pagination, and complex site structures. Step 2: Neon creates a PostgreSQL database with proper schemas: CREATE TABLE products (id, name, price, category, source_url, scraped_at).
Indexes on price, category, scraped_at. The data goes into a real database , not a CSV, not a JSON file, not a Google Sheet.
A proper relational database you can query with SQL. Step 3: SQL queries extract insights. 'Average price by category this month vs last month' runs as a real SQL query with JOINs, GROUP BYs, and window functions.
Results go to Sheets as an analytical dashboard. The database scales to zero when idle , you pay nothing when you are not querying.
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Neon, Firecrawl and Google Sheets so your AI agent scrapes structured data from any website using Firecrawl, stores it in a Neon serverless PostgreSQL database with proper schemas and indexes, and builds analytical dashboards in Sheets from SQL queries.
Neon Serverless Postgresql
actionServerless PostgreSQL that scales to zero , stores scraped data in proper relational schemas with full SQL query power
run_sql list_databases list_tables describe_table get_connection_string Firecrawl
triggerExtracts structured data from any website , handles JavaScript rendering, pagination and anti-bot measures
scrape_url crawl_url search extract_data check_crawl_status Google Sheets
enrichmentVisualizes SQL query results as analytical dashboards and reports
create_spreadsheet update_sheet_values append_sheet_values get_sheet_values Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own MCP
Turn any internal API into an MCP server. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Neon Serverless Postgresql, Firecrawl & Google Sheets ready in the catalog right now
- Add more from 4,700+ servers whenever you need
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added every week
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 4,700+ MCP servers and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across every tool, in one conversation. That's what this infrastructure was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across every platform.
Contextual Reasoning
Every decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your MCP servers once, and your agent handles the rest. Every time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius MCP catalog to handle these for you. You describe what you need. It does the rest.
AI builders creating queryable databases from web data without managing database infrastructure
Researchers building longitudinal datasets from web scraping with SQL-powered analysis
Product teams tracking competitor pricing in a real database instead of spreadsheets
AI enthusiasts building personal data warehouses from niche web sources with zero DevOps
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need?
Three: Neon, Firecrawl and Google Sheets.
Does this work with Claude Desktop?
Yes. Any MCP-compatible AI client works.
Does Neon really scale to zero?
Yes. Neon serverless PostgreSQL suspends after inactivity and resumes in ~500ms on next query. Zero cost when idle.
Is my data secure?
MCP servers authenticate via API keys. Neon stores data in your database. Firecrawl scrapes public web content.
Track Database Performance Issues Using MCP
Query performance profiled, slow queries caught, branch costs tracked, optimization reports generated , DBA-level visibility without a DBA
MCP Servers for Self-Updating Research Bases
You spend 3 hours reading 40 articles to write one research brief , an AI agent with Firecrawl reads all 40 in 90 seconds, stores them semantically in Weaviate, and writes the brief in Notion with every source linked and every claim verified
Benchmark Seed Valuations Using MCP Servers
Your portfolio valuations compared, market comps pulled, benchmark report built , know if $12M pre-money for a Seed is reasonable before you negotiate
Book Appointments via WhatsApp Using MCP
Your AI agent checks availability, sends time slots via WhatsApp and logs every booking
Calculate Your Real Meeting Costs Using MCP
Your team has 340 hours of meetings this week across 47 events , and nobody has calculated that this costs $28,000 in engineering salaries just to sit in rooms and nod
Consolidate Scattered Knowledge Using MCP
Half your documentation is in Notion and half is in Coda because two teams chose different tools , now nobody can find anything and onboarding a new engineer takes 3 weeks instead of 3 days
MCP servers used in this workflow
Neon (Serverless PostgreSQL)
Neon (Serverless PostgreSQL) MCP Server manages your entire serverless Postgres stack through conversation. Spawn zero-copy branches for isolated testing, audit project resource usage, and provision new databases without touching the CLI. It lets you manage connection endpoints, roles, and schemas by talking to your AI client.
Firecrawl
Firecrawl. Turn any website into clean, LLM-ready Markdown with a single API call. This server lets your AI agent scrape single pages, crawl entire sites, map site structures, and search the live web—all into structured data for processing. Stop dealing with messy HTML and start feeding clean content directly to your models.
Google Sheets
Google Sheets MCP Server lets your AI client read, write, and manage data directly in Google Sheets. Use conversational commands to pull data from specific ranges, append new rows, or structure entire spreadsheets. It acts as an analyst, letting you manipulate complex data without opening the GUI or writing formulas.