QuestDB (Time-Series) MCP Server for LlamaIndexGive LlamaIndex instant access to 4 tools to Execute Sql, Export Data, Import Data, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add QuestDB (Time-Series) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The QuestDB (Time-Series) MCP Server for LlamaIndex is a standout in the Databases category — giving your AI agent 4 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to QuestDB (Time-Series). "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in QuestDB (Time-Series)?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine QuestDB (Time-Series) tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 sql on QuestDB (Time-Series)
Use this for standard SELECT, INSERT, or DDL operations. Execute SQL statements (queries, DDL, DML) on QuestDB
Export data on QuestDB (Time-Series)
Useful for extracting large datasets. Export query results as CSV or Parquet
Import data on QuestDB (Time-Series)
Automatically creates tables and columns if they do not exist. Import tabular data (CSV, TSV) into a table
Ping on QuestDB (Time-Series)
Health check and version information
Connect QuestDB (Time-Series) to LlamaIndex via MCP
Follow these steps to wire QuestDB (Time-Series) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the QuestDB (Time-Series) MCP Server
LlamaIndex provides unique advantages when paired with QuestDB (Time-Series) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine QuestDB (Time-Series) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain QuestDB (Time-Series) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query QuestDB (Time-Series), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what QuestDB (Time-Series) tools were called, what data was returned, and how it influenced the final answer
QuestDB (Time-Series) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the QuestDB (Time-Series) MCP Server delivers measurable value.
Hybrid search: combine QuestDB (Time-Series) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query QuestDB (Time-Series) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying QuestDB (Time-Series) for fresh data
Analytical workflows: chain QuestDB (Time-Series) queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for QuestDB (Time-Series) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting QuestDB (Time-Series) to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQuestDB (Time-Series) + LlamaIndex FAQ
Common questions about integrating QuestDB (Time-Series) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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