Apache IoTDB MCP Server

The Apache IoTDB MCP Server bridges the gap between conversational AI assistants and time‑series data stored in Apache IoTDB. By exposing a set of well‑defined tools over the Model Context Protocol, it lets an AI client query and manipulate IoTDB data without requiring direct database access or manual SQL execution. This is particularly valuable for developers building AI‑augmented analytics dashboards, real‑time monitoring systems, or automated reporting pipelines where the assistant can fetch and transform data on demand.

What Problem Does It Solve?

Many IoT and sensor‑driven applications generate large volumes of time‑series data that must be queried, aggregated, and visualized. Traditionally, developers write custom scripts or use heavy BI tools to pull this data from IoTDB. The MCP server eliminates that friction by providing a standardized, protocol‑agnostic interface: an AI assistant can issue high‑level queries (e.g., “show devices in ”) and receive structured results, all while remaining agnostic to the underlying database engine. This decouples data access from application logic and enables rapid prototyping of AI‑driven insights.

Core Capabilities

• Dual SQL Dialect Support
  The server supports both the Tree and Table dialects of IoTDB, allowing users to choose the model that best fits their data schema. The configuration flag switches between them.

• Metadata and Data Retrieval
  Tree tools (, ) let the assistant read schema information and perform aggregations such as SUM, COUNT, AVG, and variance. Table tools () provide straightforward SELECT access in the table model.

• Export Functionality
  Both dialects expose export tools (, ) that stream query results to CSV or Excel files, optionally naming the output. The server returns a preview (first 10 rows) to confirm correctness.

• Schema Exploration
  In the table model, and give the assistant a programmatic view of available tables and their column types, enabling dynamic query construction.

Real‑World Use Cases

• IoT Analytics – An AI assistant can pull sensor readings, compute rolling statistics, and generate alerts directly from IoTDB.
• Dynamic Dashboards – BI tools can query the MCP server to refresh charts on user demand without embedding database credentials.
• Automated Reporting – Scheduled reports can be generated by having the assistant run export queries, delivering CSV/Excel files to stakeholders.
• DevOps Monitoring – Infrastructure metrics stored in IoTDB can be queried via the assistant to surface trends or trigger remediation actions.

Integration with AI Workflows

The server’s tools are lightweight JSON‑based RPC calls, making them easy to invoke from any MCP‑compatible client. Developers can embed the MCP server into a larger AI pipeline, letting the assistant orchestrate data retrieval, transformation, and presentation—all while keeping database credentials secure on the server side. Because the MCP protocol is stateless, multiple assistants or services can concurrently query IoTDB without contention.

Unique Advantages

• Dialect Flexibility – Switching between Tree and Table models is a single configuration change, supporting legacy IoTDB setups as well as newer table‑based schemas.
• Built‑in Export – The ability to export directly to CSV or Excel reduces the need for external ETL tools.
• Zero Resource Exposure – The server deliberately exposes no resources or prompts, keeping the interface focused purely on data operations and reducing surface area for security concerns.

In summary, the Apache IoTDB MCP Server equips AI assistants with a robust, secure, and flexible gateway to time‑series data, enabling developers to build intelligent applications that can query, analyze, and export IoTDB content with minimal boilerplate.