QuestDB (Time-Series) MCP Server for LangChainGive LangChain instant access to 4 tools to Execute Sql, Export Data, Import Data, and more

Name: QuestDB (Time-Series)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LangChain is the leading Python framework for composable LLM applications. Connect QuestDB (Time-Series) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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The QuestDB (Time-Series) MCP Server for LangChain is a standout in the Databases category — giving your AI agent 4 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "questdb-time-series": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using QuestDB (Time-Series), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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

About 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.

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.

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.

The QuestDB (Time-Series) MCP Server exposes 4 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 4 QuestDB (Time-Series) tools available for LangChain

When LangChain connects to QuestDB (Time-Series) through Vinkius, your AI agent gets direct access to every tool listed below — spanning time-series, sql, data-ingestion, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

execute

Execute sql on QuestDB (Time-Series)

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

export

Export data on QuestDB (Time-Series)

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

import

Import data on QuestDB (Time-Series)

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

action

Ping on QuestDB (Time-Series)

Health check and version information

Connect QuestDB (Time-Series) to LangChain via MCP

Follow these steps to wire QuestDB (Time-Series) into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 4 tools from QuestDB (Time-Series) via MCP

Why Use LangChain with the QuestDB (Time-Series) MCP Server

LangChain provides unique advantages when paired with QuestDB (Time-Series) through the Model Context Protocol.

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

QuestDB (Time-Series) + LangChain Use Cases

Practical scenarios where LangChain combined with the QuestDB (Time-Series) MCP Server delivers measurable value.

RAG with live data: combine QuestDB (Time-Series) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query QuestDB (Time-Series), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain QuestDB (Time-Series) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every QuestDB (Time-Series) tool call, measure latency, and optimize your agent's performance

Example Prompts for QuestDB (Time-Series) in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with QuestDB (Time-Series) immediately.

"Check if the QuestDB server is online and show me the version."

"Execute a query to find the average temperature from the 'sensors' table for the last hour."

"Export the last 1000 rows of the 'trades' table as a CSV file."

Troubleshooting QuestDB (Time-Series) MCP Server with LangChain

Common issues when connecting QuestDB (Time-Series) to LangChain through Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

QuestDB (Time-Series) + LangChain FAQ

Common questions about integrating QuestDB (Time-Series) MCP Server with LangChain.

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.

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.