4,000+ servers built on vurb.ts
Vinkius
LangChainFramework
QuestDB (Time-Series) MCP Server

Bring Time Series
to LangChain

Learn how to connect QuestDB (Time-Series) to LangChain and start using 4 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

MCP Inspector GDPR Free for Subscribers
Execute SqlExport DataImport DataPing

Compatible with every major AI agent and IDE

ClaudeClaude
ChatGPTChatGPT
CursorCursor
GeminiGemini
WindsurfWindsurf
VS CodeVS Code
JetBrainsJetBrains
VercelVercel
+ other MCP clients
QuestDB (Time-Series)

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

  1. Subscribe to this server
  2. Provide your QuestDB URL and optional credentials (Username/Password or Token)
  3. 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)

execute_sql

Use this for standard SELECT, INSERT, or DDL operations. Execute SQL statements (queries, DDL, DML) on QuestDB

export_data

Useful for extracting large datasets. Export query results as CSV or Parquet

import_data

Automatically creates tables and columns if they do not exist. Import tabular data (CSV, TSV) into a table

ping

Health check and version information

Why LangChain?

LangChain's ecosystem of 500+ components combines seamlessly with QuestDB (Time-Series) through native MCP adapters. Connect 4 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

  • The largest ecosystem of integrations, chains, and agents. combine QuestDB (Time-Series) MCP tools with 500+ LangChain components

  • Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

  • LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

  • Memory and conversation persistence let agents maintain context across QuestDB (Time-Series) queries for multi-turn workflows

See it in action

QuestDB (Time-Series) in LangChain

AI AgentVinkius
High Security·Kill Switch·Plug and Play
Why Vinkius

QuestDB (Time-Series) and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect QuestDB (Time-Series) to LangChain 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.

4,000+MCP Servers ready
<40msCold start
60%Token savings
Raw MCP
Vinkius
Server catalogFind and host yourself4,000+ managed
InfrastructureSelf-hostedSandboxed V8 isolates
Credential handlingPlaintext in configVault + runtime injection
Data loss preventionNoneConfigurable DLP policies
Kill switchNoneGlobal instant shutdown
Financial circuit breakersNonePer-server limits + alerts
Audit trailNoneEd25519 signed logs
SIEM log streamingNoneSplunk, Datadog, Webhook
HoneytokensNoneCanary alerts on leak
Custom domainsNot applicableDNS challenge verified
GDPR complianceManual effortAutomated purge + export
Enterprise Security

Why teams choose Vinkius for QuestDB (Time-Series) in LangChain

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 LangChain 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.

QuestDB (Time-Series)
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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

The Vinkius Advantage

How Vinkius secures QuestDB (Time-Series) for LangChain

Every tool call from LangChain to the QuestDB (Time-Series) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start
Ed25519Signed audit chain
60%Token savings
FAQ

Frequently asked questions

01

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.

02

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.

03

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.

04

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

05

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.

06

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

07

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

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