Track Database Performance Issues Using MCP.
Query performance profiled, slow queries caught, branch costs tracked, optimization reports generated , DBA-level visibility without a DBA
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent reads Neon project details: 3 projects, 7 branches, 4 active endpoints. The production branch for `app-db` has been running for 18 hours today with 2.4M queries processed.
The agent ingests query performance metrics into Axiom and runs an analysis: the slowest query is a JOIN across `orders` and `line_items` averaging 340ms , it runs 12,000 times per day.
That is 4,080 seconds of compute time daily on one query. Second slowest: a full table scan on `user_sessions` at 180ms, 8,000 executions.
The agent checks branch utilization: the `feature/payment-refunds` branch has been idle for 6 days , still consuming compute credits. The staging branch has 3 endpoints but only 1 receives traffic.
It writes to Google Sheets: query performance rankings, branch utilization, cost projections, and optimization recommendations. 'Add index on orders(user_id, created_at) , estimated 70% reduction on the JOIN query.
Suspend feature/payment-refunds branch , saving $4.20/week in idle compute.'
MCP Server Orchestration: 3 MCP Servers, one intelligent agent
Connect Neon, Axiom and Google Sheets MCP servers so your AI agent monitors your serverless PostgreSQL branches, ingests query performance data into Axiom for analysis, identifies slow queries and index opportunities, and writes weekly database health reports to Google Sheets. Backend teams running Neon for their PostgreSQL workloads get automated query profiling and cost tracking without hiring a DBA or setting up pg_stat_statements manually. One prompt and your database performance is documented.
Neon Serverless Postgresql
triggerReads branch details, database configs and endpoint status
list_projects get_project list_branches list_databases list_endpoints Axiom
actionQueries performance logs, identifies slow queries and trends
run_query ingest_data list_datasets create_dataset Google Sheets
actionWrites weekly database health reports and cost projections
update_sheet_values append_sheet_values create_spreadsheet get_spreadsheet 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, Axiom & 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.
Backend teams running Neon serverless PostgreSQL who need automated query performance monitoring without configuring pg_stat_statements
Engineering managers tracking Neon compute costs across multiple projects and branches who need per-branch cost attribution
Solo developers who need DBA-level query optimization recommendations without the expertise to analyze query plans manually
Platform teams managing staging and feature branches in Neon who want automated idle branch detection and cleanup reminders
Frequently Asked Questions About This MCP Server Orchestration
Which MCP servers do I need for this workflow?
Three: Neon Serverless PostgreSQL, Axiom and Google Sheets. Connect all three to your AI client.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others.
How does the agent get query performance data?
The agent reads Neon project and endpoint metadata, then uses Axiom to query and analyze performance logs ingested from your application.
Can I use this with standard PostgreSQL instead of Neon?
The branch and endpoint management is Neon-specific. For standard PostgreSQL, you can still use Axiom + Google Sheets for query analysis.
Does it actually save money?
Idle branch detection alone typically saves $8-20/week for teams with 3+ feature branches. Index optimizations reduce compute time, which directly reduces Neon costs.
Can I get alerts for slow queries instead of weekly reports?
Yes. Ask the agent to check for queries exceeding your threshold (e.g., 500ms average) and post alerts to Discord or create Axiom monitors.
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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.
Axiom
Axiom. Connect your AI client to Axiom to manage logs and observability data. Ingest JSON, NDJSON, or CSV data and run complex Axiom Processing Language (APL) queries to analyze logs in real-time. You can manage datasets, create monitors, and track system errors directly through natural conversation with your agent.
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.