Bring Time Series
to CrewAI
Learn how to connect QuestDB (Time-Series) to CrewAI 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 CrewAI?
When paired with CrewAI, QuestDB (Time-Series) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call QuestDB (Time-Series) tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter and agents auto-discover every available tool at runtime - —
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
- —
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
QuestDB (Time-Series) in CrewAI
QuestDB (Time-Series) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect QuestDB (Time-Series) to CrewAI 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 CrewAI
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 CrewAI 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 CrewAI
Every tool call from CrewAI 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 CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
Can different agents in the same crew use different MCP servers?
Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
What happens when an MCP tool call fails during a crew run?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
Can I run CrewAI crews on a schedule (cron)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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