Bring Time Series
to Vercel AI SDK
Learn how to connect QuestDB (Time-Series) to Vercel AI SDK and start using 4 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the QuestDB (Time-Series) MCP Server?
Connect your QuestDB instance to any AI agent to perform high-speed time-series analysis and data management using natural language.
What you can do
- SQL Execution — Run complex SQL queries, DDL, and DML operations optimized for time-series data.
- High-Speed Ingestion — Import tabular data (CSV/TSV) directly into tables with automatic schema creation and partitioning.
- Data Export — Extract large datasets in CSV or Parquet formats for external analysis or reporting.
- Health Monitoring — Instantly check server status and version information to ensure your database is operational.
How it works
- Subscribe to this server
- Provide your QuestDB URL and optional credentials (Username/Password or Token)
- Start querying and managing your time-series data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Engineers — Quickly inspect table schemas, run migrations, and verify data ingestion pipelines.
- Analysts — Perform ad-hoc time-series analysis and export results without writing complex scripts.
- DevOps Teams — Monitor database health and perform maintenance tasks through a conversational interface.
Built-in capabilities (4)
Use this for standard SELECT, INSERT, or DDL operations. Execute SQL statements (queries, DDL, DML) on QuestDB
Useful for extracting large datasets. Export query results as CSV or Parquet
Automatically creates tables and columns if they do not exist. Import tabular data (CSV, TSV) into a table
Health check and version information
Why Vercel AI SDK?
The Vercel AI SDK gives every QuestDB (Time-Series) tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 4 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
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TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
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Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same QuestDB (Time-Series) integration everywhere
- —
Built-in streaming UI primitives let you display QuestDB (Time-Series) tool results progressively in React, Svelte, or Vue components
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Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
QuestDB (Time-Series) in Vercel AI SDK
QuestDB (Time-Series) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect QuestDB (Time-Series) to Vercel AI SDK through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for QuestDB (Time-Series) in Vercel AI SDK
The QuestDB (Time-Series) MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 4 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Vercel AI SDK only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
QuestDB (Time-Series) for Vercel AI SDK
Every tool call from Vercel AI SDK to the QuestDB (Time-Series) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I execute standard SQL queries and DDL commands like creating tables?
Yes! Use the execute_sql tool to run any valid QuestDB SQL statement, including SELECT, INSERT, and table definitions. You can also include parameters like explain to see the execution plan.
How do I import a CSV file into a new or existing table?
Use the import_data tool. Provide the target table name and the raw CSV data. The tool can automatically create the table structure and handle partitioning if specified.
Is there a way to export large query results for use in other tools?
Absolutely. The export_data tool allows you to run a query and receive the output in CSV or Parquet format, which is ideal for large-scale data extraction.
How does the Vercel AI SDK connect to MCP servers?
Import createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.
Can I use MCP tools in Edge Functions?
Yes. The AI SDK is fully edge-compatible. MCP connections work on Vercel Edge Functions, Cloudflare Workers, and similar runtimes.
Does it support streaming tool results?
Yes. The SDK provides streaming primitives like useChat and streamText that handle tool calls and display results progressively in the UI.
createMCPClient is not a function
Install: npm install @ai-sdk/mcp
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